Loading...

A Comparative Study of Two Methods for Age Determination Using Nubian Dental Sample

Master's Thesis 2010 148 Pages

Pedagogy - Science, Theory, Anthropology

Excerpt

Table of Contents

Abstract

Acknowledgments

Glossary of Abbreviations

List of Table

List of Figures

List of Graphs

1 INTRODUCTION
1.1 Overview
1.2 Inherent problemstoadultage-at-deathassessmentfromthe skeleton
1.3 Justificationfortheresearch
1.4 Statement ofpurpose
1.5 Organization of the thesis

2 REVIEW OF LITERATURE
2.1 Introduction
2.2 Age estimation and dental age related changes
2.3 Rootdentine translucencyinageestimation
2.3.1 Ontogenyand Physiology of Root DentineTranslucency
2.3.2 ReviewoftheLiterature on AgeEstimationusingroot dentine translucency
2.3.3 Root dentine translucency in archaeological contexts
2.4 Dentalrootcolorinageestimation.
2.4.1 Review ofthe Literature on Age Estimation using dental root color

3 MATERIALS AND METHODS
3.1 Dental charting method
3.2 Sample derivation and characteristics
3.3 Protocol for sample collection
3.3.1 Sample selection
3.3.2 Extraction , Disinfection and Storage
3.4 Protocol for sample preparation for root dentine translucency and dental root color analysis
3.4.1 Root dentine translucency measurement
3.4.1.1 Manual measurement of root dentine translucency RDT (the traditional method)
3.4.1.2 Image analysis measurements of root dentine translucency
3.4.2 Color analysis and description
3.5 Protocol for Statistical analysis for root dentine translucency and dental root color analysis

4 RESULTS
4.1 Root dentine translucency
4.2 Color analysis
4.3 Regression analysis
4.4 Validation ofthe regression equations
4.4.1 Application to published data
4.4.2 Validation ofthe results using resamplingtechniques
4.4.3 Hold out cross validation
4.5 The effect of sex on RDT and RGB.

5 Discussion and Conclusions

List of References

Abstract

stimation of adult skeletal age-at-death is not only one of the most important identifying features for an unknown individual but also one of the most difficult to achieve especially among old adults. The main source of the problem is the nature of human aging. This feature is characterized by an accumulation of metabolic disorders that show great variation in the level and the degree of change with increasing age both within and between populations. Moreover, individual aging is determined by the interplay of complex set of genetic, culture and environmental factors.

Variation in the biological aging process has profound effects on age-at- death assessment. The relationship between chronological age and skeletal age indicators is neither constant nor linear. The assumption that the underlying biological basis of the age-indicator relationship is constant across different populations is erroneous. Therefore, it is necessary to take into account that age changes are not uniform across populations.

It has been demonstrated that when applied an aging method to independent populations of known age at death, it proves less reliable than the results obtained when applying the method to samples from the population used to formulate the methods. Moreover most adult skeletal aging methodologies involve placing a skeletal element into a phase category. This type of phase-oriented age estimation, leads to several problems namely 1) observer subjectivity, 2) large age ranges and open-ended intervals , 3) stages that overlap one another, 4) aging bias, 5) age mimicry, 6) preservation problems that lead to missing data, and 7) improper theoretical and statistical methodology used to derive estimate age-at-death. To overcome these problems dental age estimation techniques have been continuously revised and improved.

Dental tissues are the most durable substances produced by the human body and show the best resistance against postmortem alterations caused by humidity, temperature, mechanical and microbial activities. Moreover, several techniques for dental age estimation are reported to be more accurate than methods based on other skeletal elements. Dental age estimation methods are either based on the well-ordered cascade of changes that occur during the formation and eruption of teeth or on continuous processes that alter and diminish the quality of dental tissues.

The purpose of the present study was to compare and evaluate two dental macroscopic age estimation criteria. Namely root dentine translucency (RDT) and dental root color measured as a mean value of the red, blue, and green components of color (RGB). The sample consisted of 416 freshly extracted single rooted permanent teeth collected from 311 individuals referred to several dentists and dental clinics in Kom Ombou district, south Egypt in the Nubian resettlement area. RDT and RGB were assessed using traditional (manual) and image analysis quantification techniques.

Analysis reveals that there is high linear correlation between both RDT and RGB values with age, with r = 0.951 and -0.985 respectively. Regression analyses yields a new set of equations to estimate age among Egyptian Nubian population with high correlation values ranging from r = 0.90 to 0.97 and a standard error of the estimate ranging from ± 5.067 to ±2.996. The best estimates were obtained by combining both RGB and RDT. Additionally, the analysis has shown that sex of the individual don’t affect age assessment.

It was concluded that RDT and RGB can be used to assess age at death with high accuracy. Moreover, this technique can form a basis of practical application in establishing age at death both in forensics and paleodemographic aspects.

Acknowledgements

P raise is to GOD, Lord of the universe. Most Gracious, Most Merciful. Words will never be enough to express how I am deeply thankful to him, without whose guidance and will, this work would not grow a reality. It was his blessings that made this work completed.

I would like to thank my supervisors for their valuable feedback, not only in preparation of this thesis, but for insight, knowledge, and clarification over the past several years. I would especially like to thank Dr. Moheb Shaaban for encouraging me to pursue this Field of Anthropology and for all of his countless hours of assistance and review of previous drafts. He has been an integral part of the success of this thesis and I will be a better researcher, teacher, and anthropologist because of his dedication. I cannot thank him enough for believing in me and encouraging me to pursue my dreams. For which I extend my gratitude.

I would also like to thank Dr. Nancy Khattab. She offered much encouragement and advice on the completion of this research, for which I am grateful.

This research could not have been accomplished without the dental material analyzed. I would like to thank all my fellow dentists who have helped me in collecting the sample used in this research, to all my fellow colleagues in Kom Ombou area with whom I have spent a great time. For whom I am grateful.

I would like to thank my professors in the Institute of African research and studies who encouraged and supported me in every aspect. I would like to thanks my parents who encouraged and supported me in every aspect of my graduate career: emotionally, spiritually. And I can never tell or show you how grateful I truly am for all of your support.

Glossary of abbreviations

Abbildung in dieser Leseprobe nicht enthalten

List of Tables

Table3.1 Sample distributionaccordingtotoothtypeandsex

Table3.2 Sample distribution according to chronological age and 53 sex

Table4.1 spearman rho and Kendall tau correlation coefficients 72 matrix between chronological age (CA) and root dentine translucency manually measured (RDTM) and computer assisted measurements (RDTC) and root dentine translucency index (RDTI)

Table4.2 spearman rho and Kendall tau correlation coefficients 74 matrix between (CA) and RGB values

Table4.3 correlation coefficients between published age CA and 78 age estimates using RDT and RDTI equations driven in this study

Table4.4 spearman rho and Kendall tau correlation coefficients 83 between CA and age estimates from different regression equations

Table4.5 Spearman rho and Kendall tau correlation coefficients 86 between MRDTF, FRDTF , MRGBF, FRGBF

List of Figures

Fig 1.1 Research categories of dental anthropology

Fig 1.2 Factors affecting human dentition

Fig 1.3 Data revealed by study of human teeth

Fig 1.4 Age estimation from the sacrum and The six phases of the Suchey-Brooks System (SBS) for the pubic symphysis for males

Fig 1.5 Age estimation from different skeletal locations

Fig 1.6 Age estimation using cranial suture closure

Fig 2.1 Anatomyofamandibularfirst premolar, showing different dental tissues

Fig 2.2 A comparison between teeth belonging to old and young individuals, the decrease in the size of the pulp is due to deposition of secondary dentine

Fig 2.3 Annulations shown in cementum as an age change

Fig 2.4 Ginvigalressecionwithadvancementinage,notetheexposure ofa progressive area ofthe dental root and loss of bone and periodontal support

Fig 2.5 Dental attrition and dental attrition scoring model for age estimation ( Brothwell, 1981)

Fig 2.6 Ground section in a premolar showing progressive root dentine translucency

Fig 2.7 Late bell Stage and structure of dentine (Ten cate, 1998)

Fig 2.8 Different methods of assessing root dentine translucency as described in literature (intact, with the aid of light box, half sectioned, ground sectioned)

Fig 2.9 Scoring of different morphological changes in the structure of 24 the tooth according to Gustafson

Fig 2.10 Scoring of different morphological changes in the structure of the tooth according to Johanson 1970

Fig 2.11 Measurements proposed by Lamendin for age estimation, root height RH, periodontal regression P and translucency ofthe root RDT

Fig 2.12 Intactteethunderalightmicroscopead-justedtox10 magnification, with a ruler having 0.001 mm grades according to Saglam Atsu et al., (2006)

Fig 3.1 Different dental charting methods for permanent and deciduous teeth

Fig 3.2 A map of New Nubia showing the sites of sample collection.

Fig 3.3 SamplecollectionchartandDatacodingschemeused inthe present study

Fig 3.4 Teeth stored in small plastic tubes filled with distillated water

Fig 3.5 Instruments used in manual measurement of RDT

Fig 3.6 Different measuring technique of RDT according to the morphological pattern of RDT

Fig 3.7 Digitalmeasurementof RDTusing Digimizer3.6

Fig 3.8 Applicationofimageenhancementtechniquesfor Computer assisted measurement of root dentine translucency

Fig 3.9 Additive and subtractive color spaces

Fig 3.10 TheRGB colorsystem andthedifferencebetweenanalogue and digital recording of color

Fig 3.11 coloranalysisofthe root

Fig 3.12 Statistical analysis in the current research

List of Graphs

Graph 3.1 Sampledistributionaccordingto toothtypeandsex

Graph 3.2 Sampledistributionaccordingto chronologicalage andsex

Graph 4.1 Histogram of the agedistribution ofthesample, witha QQ 70 plot to assess the normality of the sample distribution

Graph 4.2 A3D border displaychartshowing the relationbetweenCA and RDTM, RDTC against each other

Graph4.3 A3D border displaychartshowing the relationbetweenCA and RDTI left graph and RDTM, RDTC right graph

Graph4.4 A3D border displaychartshowing the relationbetweenCA and R, G, B and CA and RGBI, RGB

Graph 4.5 A: residuals ofthe regression between CA and RDT, RDTI, B: residuals ofthe regression between CAand RGB,RGBI C: a sequentional plot of the relation between CA and RDTM, RDTC,RDTI (right) and CA and RGB,RGBI (left)

Graph4.6 A:sequentional graphbetweenCAandAERDT,AERDTI B: A box plot showing the median and the distribution of CA, AERDT, AERDT where CA refers to published data

Graph 4.7 Jackknifed spearman correlations between CA and RDT, RDTI, RGBI

Graph 4.8 Bootstrapped spearman correlations between CA and RDT, RDTI,RGBI

Graph 4.9 Thepercent ofthevalidatingandthetrainingsamples

Graph 4 .10 A box plot showing median and distribution of CA and age estimates from different regression equations

Graph 4.11 sequentional graph between CA age estimates from different regression equations.

Graph 4.12 A 3D border display chart showing the relation between MRDTF, FRDTF and MRGBF, FRGBF

Graph 4.13 Histograms ofthe different rates of root dentine translucency formation between males and females

Graph 4.14 Histograms of the different rates of dental root color change between males and females

Chapter 1 Introduction

1.1 Overview

The term "dental anthropology" first appears in the title of an article published in 1900 by George Buschan, although Klatsky & Fisher (Klatsky and Fisher, 1953) are credited with its formal introduction (Pedersen et al., 1967), teeth possess qualities valuable for anthropological study they are durable; evolutionarily conservative and yet adaptable; rich with genetically determined traits; and reflective of behavior, ecology, and diet. Research categories in dental anthropology include morphology, metrics, health, evolution, growth, genetics, usage, forensics, and ethnographic treatment. (Fig 1.1) (Scott & Turner, 1988)

Human teeth have several advantages; the single most obvious one is its durability. Tooth composed of the hardest and most durable tissue in the body and therefore teeth can survive adverse conditions of preservation for longer than any other parts of the skeleton. Tooth enamel grows during childhood and does not change morphologically or chemically later, thereby providing a permanent record of some critical events of childhood, such as episodes of malnutrition stresses or disease. Furthermore, dental development is a valuable indicator of the age of an individual at death. Besides their importance for age estimations, the form and condition of a person’s teeth may reveal much information concerning health status, biological relations, nutrition and the use of teeth as tools. ( Fig 1.2,1.3) (Scott & Turner 1988, Salo 2005)

Fig 1.1 Research categories of dental anthropology

Abbildung in dieser Leseprobe nicht enthalten

Fig 1.2 Factors affecting human dentition

Abbildung in dieser Leseprobe nicht enthalten

Fig 1.3 Data revealed by study of human teeth

Abbildung in dieser Leseprobe nicht enthalten

1.2 Inhexęnt_pxGbljemsi_toi_adultiage-at-deathiassess_ment_frgmithe skeleton.

Skeletally based methods of age determination are broadly split into two major groups: 1) those which are based upon developmental changes in young individuals, 2) and those which are based upon degenerative changes of the individuals which have reached full skeletal and dental maturity. For individuals who died before full maturity was reached several different methods exist based upon the developmental sequence of different parts of the skeleton. McKern and Stewart (1957) used epiphyseal union of the major long bones to estimate age of males in the 18-24 year age group. Anderson et al. (1964) used the lengths of long bones from children to draw up tables of mean lengths for ages in the 1-18 year range using one year age increments. The major problem with these methods of ageing is with sex. Generally females mature by some two to three years ahead of males. Therefore, there will always be some error in any technique. However, the overall picture for age estimation for young individuals is satisfactory, with good precision and reliability (Bass, 1987).

This is because juveniles undergo relatively large changes in a short period of time, and the small fluctuations and variations in the development sequence are going to make little difference to the estimated age (Angel, 1969). Moreover, it is well established that nutrition is a critical factor that plays a major role in human growth and development of the individual. However, as long as an individual is not experienced extremes of nutritional deficiency, then the body will develop at more or less the same rate as those who are biologically similar (Prince ,2004).

On the other hand, age assessment from adult skeletons represents some problems. It is well known that methods of aging adults are not equally reliable, and accuracy of age estimation drops considerably with advancing age, as more factors come into play. Metabolism for example, although biologically determined, varies greatly from individual to individual within a given human population. This has the effect that a short, plump, highly stressed fast maturer can look some ten to twenty years older than his chronological age, on the other hand a slim, low stressed, slow maturer can look five to fifteen years younger than his chronological age (Angel, 1984).

Moreover, living circumstances can lead to a greater variability. Dietary deficiency in someone forty years old has had forty years in which to make its impact on the human skeleton, compared to someone who has not had that deficiency then there will be increasing divergence with time in any affected skeletal property. Lastly there is a tendency of the human body to cease systematic change, changes becoming in effect random, at about the age of fifty (Ortner, 1981). All these factors are reflected in the standard skeletal methods of age at death determination.

In their re-evaluation of Todd's method of age determination (1920,1921); Katz and Suchey (1986) used pubic bone morphology as a means of predicting age of death( Fig 1.4). Using Katz and Suchey’s tables it is possible to see the increase in variability with increase in age, this is reflected as increasing standard deviation. It becomes impossible to discriminate between individuals of different ages who are older than fifty. Similarly, Gilbert and McKern's 1973 attempt to extend McKern and Stewart’s (1957) ageing method to females found large variability in the method, again the variability increases with age, and yet again distinguishing between individuals of different ages was not possible beyond the mid-fifties. Reliable estimates have been claimed for a method developed by Lovejoy et al., (1985), who developed a technique based upon the morphological characteristics of the auricular surface of the ileum. This method seems to give consistent variability across the age ranges, and is extendible up to the age of sixty. Other methods of age determination from the skeleton exist (Fig 1.5): sternal end of rib ossification (Iscan et al., 1984, 1985), cranial suture closure (Ascadi & Nemeskeri 1980, Meindl & Loveyjoy 1985) (Fig 1.6); although the population used to develop this method (reference population) is felt to have unreliable age at death data. These methods suffer from very much the same problems as pubic morphology and auricular surface in that they are all based upon broad banded phases of development, the morphological traits of which are not very distinct from one phase to another.

Means for dealing with this problem have been suggested (Lovejoy et al., 1985), and usually revolve around modifying the age estimate in the light of secondary morphological characteristics. This leads to the suggestion of subjectivity playing a major part in skeletally based methods of determining age at death (Konigsberg & Frankenberg, 1992).With these types of methods, physical anthropologists must subjectively place a skeletal element into an ordinal phase category. In so doing, there are several problems which arise: 1) the subjectivity of the observer leads to problems with inter- and intra-observer error; 2) large age ranges are produced when these types of phase-aging methods are utilized, in some cases a range may cover most of adult age (Suchey-Brooks Phase V: 25-83 years) and in several phase oriented aging methods, the last phase is an open-ended interval, for example, 50+ (Todd phase 10); 3) stages often overlap one another; 4) bias in overestimating age in younger individuals while underestimating age in older individuals occurs quite frequently; 5) age mimicry occurs when appropriate reference samples are not utilized and thus increases error estimates; 6) preservation problems lead to missing data, and 7) improper theoretical and statistical methodology has often been used to derive age-at-death estimates. (Prince, 2004)

Fig 1.4 Age estimation from the six phases of the Suchey-Brooks System (SBS) for the pubic symphysis for males note the wide age ranges phase V and phase VI( Buikstra and Ubelaker, 1994)

Abbildung in dieser Leseprobe nicht enthalten

Fig 1.5 Age estimation from different skeletal locations

1 Cranial suture closure (Todd and Lyon 1924, Montagu 1938, Singer 1953, Brooks 1955, Meindl and Lovejoy 1985).

2 The sternal ends of ribs (Iscan et al. 1984a, 1984b, 1985, Iscan and Loth 1986, Iscan et al. 1987).

3 The auricular surface of the os coxae (Lovejoy et al. 1985, Buckberry and Chamberlain 2002).

4 Changes in the pubic symphyseal face (Todd 1920, Todd 1921a, Todd 1921b, Brooks 1955, Nemeskéri et al. 1960, McKern and Stewart 1957, Gilbert 1973, Gilbert and McKern 1973, Suchey 1979, Meindl et al. 1985, Katz and Suchey 1986, Brooks and Suchey 1990) .

7 Dental attrition (Gustafson 1950, Murphy 1959, Miles 1962, Brothwell 1963, Molnar 1971, Helm and Prydso 1979, Scott 1979, Smith 1984a, Cross et al. 1986, Dreier 1994, Lovejoy 1985,Lovejoy et al. 1985, Dahl et al. 1989, Song and Jia 1989, Johansson et al. 1993, Kim et al. 1995, Li and Ji 1995, Ajmal et al. 2001, Ball 2002).

6 Radiology of the proximal femur and clavicle (Walker and Lovejoy 1985).

Fig 1.6 Age estimation using cranial suture closure (Buikstra and Ubelaker, 1994).

Abbildung in dieser Leseprobe nicht enthalten

1.3 Justification for the research

Bioarchaeological research must be built upon a solid foundation of accurate age and sex estimates. Unfortunately there are inherent biases which guarantee a certain amount of error in the assessment of these fundamental variables. These biases are seemingly unavoidable using any relative standard for age estimation. All age assessment methods, whether on the macroscopic or microscopic level, are inherently flawed due to a set of problems in human biology as well as anthropology. Some of the most important and most frequently discussed considerations include: 1) the distinction between chronological and biological age, 2) uniformitarian assumptions about aging through time and space, 3) environmental and genetic differences between reference and sample populations, 4) preservation, diagenetic change, and recovery of the skeleton, and 5) variance between dental and skeletal age determinations. (Prince, 2004)

Bioarchaeologists use the term biological age to recognize the discrepancy between an individual’s morphology and their chronological age, which is inaccessible for all archeological skeletal populations. The disparity between chronological and biological age has been the subject of research by both human biologists (Bittles and Collins, 1986) and paleodemographers (Paine, 1997). The discrepancy between chronological and biological age is a product of both measurable systemic reasons as well as significant idiosyncratic and seemingly random differences. There are three main sets of problems responsible for the differences between chronological and biological age: individual variation, pathological conditions, and methodological issues.

The present research compares two dental methods for age estimation, and attempts to avoid some of the pitfalls of other dental age estimation techniques suffer from.

1.4 Statement_of_purp_qse

The main purpose of the study is to compare two dental aging methods using a modern Nubian dental sample. For the present analysis, age was assessed using methods based on root dentine translucency, and dental root color. To determine whether the regression equations developed from this research were reliable and accurate for age estimation, and to assess the applicability, accuracy and reproducibility of the used techniques, the following research questions were posed:

1. ) Are the methods relatively accurate in relationship to the known chronological age?
2. ) Are there significant differences between sexes?
3. ) Are there any regression equations that can correlate these methods with chronological age with accuracy and reliability?
4. ) Can these methods be applied to samples other than the current research sample?

1.5 Organization, of the thesis

The purpose of the first chapter is mainly to provide background information that will be useful in the subsequent chapters. Chapter 2 reviews relevant literature that deals with age estimation using root dentine translucency and dental root color change.

Chapter 3 provides detailed description of the sample and methodology used for this study, including description of the sample selection criteria, methods used in the analysis for age estimation using root dentine translucency and dental root color. The protocols for preparing the sample for each phase of analysis, equipment, methods for data collection, and statistical analysis adopted.

Chapters 4 describe the findings and the results obtained from data analysis, also the regression equations driven from the sample are mentioned. Chapter 5 discusses the results and the impact of the research and summarizes the main conclusions of the thesis and suggests recommendations for future research. At the end of the thesis are the references used in the current research and an Arabic summary for the current research.

Chapter 2 BeviewofLiterature

2;1.Introduction

Age-at-death assessment faces biological and methodological problems. Age-related processes show great variation, both within and between populations. However, in anthropological contexts, this parameter is crucial for identification, and both accuracy and reliability are required (Hanihara, 1952; Brooks, 1955; Biggerstaff, 1977; Zhang, 1982; Jackes, 1985; 1993, Moore­Jansen and Jantz, 1986; Iscan et al., 1987; Katz and Suchey, 1986; Ubelaker, 1989; Konigsberg and Frankenberg, 1992; Molleson et al., 1993; Plato et al., 1994; Kemkes-Grottenthaler, 1996; Jackes, 2000; Boldsen et al., 2002; Hoppa and Vaupel, 2002b; Kemkes-Grottenthaler, 2002; Prince and Ubelaker, 2002; Ross and Konigsberg, 2002; Komer, 2003; Šlaus et al., 2003).

Dental tissues (enamel and dentine) are among the most durable substances produced by the human body. They resist different external conditions, whether they are mechanical, thermal or chemical irritations. Dental age estimation methods are either based on the well-ordered cascade of changes that occur during the formation and eruption of teeth or they rely on continuous processes that alter and diminish the quality of dental tissues even when individual growth is completed. (Ubelaker, 1989).

Several researchers have developed techniques to determine age-at-death for adults by employing the dentition tissues and dental morphology. Most methods involve assessing age-related changes in attrition (Zuhrt, 1955; Miles, 1962, 1963; Brothwell, 1963; Lavelle, 1970; Molnar, 1971; Ito 1972,1975; Lunt, 1978; Miles, 1978; Scott, 1979; Smith, 1984 a, b; Lovejoy, 1985;

Brothwell, 1989; Li and Ji, 1995), secondary dentin deposits (Morse et al., 1993; Kvaal and Solheim, 1994), cementum apposition (Charles et al., 1986; Condon et al., 1986; Wittwer-Backofen, 2000; Wittwer-Backofen and Buba, 2002; Wittwer-Backofen et al., 2004), apical translucency (Bang and Ramm, 1970), periodontal recession (Solheim, 1992; Borrman et al., 1995), root resorption (Borrman et al., 1995), acid racemization (Helfman and Bada, 1975; Helfman and Bada, 1976; Shimoyama and Harada, 1984; Ogino et al., 1985; Masters, 1986; Ritz et al., 1990; Ohtani and Yamamoto, 1991, 1992; Ritz et al., 1993; Ohtani, 1994, 1995; Ohtani et al., 1995; Carolan et al., 1997), color change of the root (Ten Cate et al., 1977; Solheim, 1988; Borrmann et al., 1995), or a combination of several of these indicators (Gustafson, 1947, 1950, 1955; Johanson, 1971; Maples, 1978; Maples and Rice, 1979; Matsikidia and Schultz, 1982; Kashyap and Koteswara Rao, 1990; Lamendin and Cambray , 1980; Lamendin et al., 1992; Solheim, 1993; Kvaal et al., 1995; Russell, 1996). Several researchers have analyzed these features individually and in conjunction.

2.2 Age related changes in the teeth

The teeth are heavily calcified organs comprising four main tissues (Beynon, 1991) (Fig 2.1):

- Enamel is the hardest, most heavily mineralized tissue in the human body and covers the coronal half of the dentine to form the hard occlusal surface.
- Dentine is a highly mineralized, quasi-vascular tissue having the processes of odontoblasts housed in its main structure. The dentine forms an armature comprising of tubules, a series of parallel tunnels running from the enamel to the pulpal chamber.
- The pulpal chamber, a cavity in the dentine which is filled with ground substance and the structures for the vascular supply of the tooth. Lining is a layer composed of the cell bodies of the odontoblasts. Other cells inhabiting the pulp chamber are fibroblasts, undifferentiated ectomesenchymal cells and macrophages.
- Cementum covers the apical half of the dentine and is similar in composition, but not structure to the dentine. This forms a base for the attachment of the tooth into the alveolar bone.

Fig 2.1 Anatomy of a mandibular first premolar, showing different dental tissues (Kvaal and Solhiem, 1994)

Abbildung in dieser Leseprobe nicht enthalten

Although, thought to be relatively quiescent organs, teeth manifest many age-related changes, and as such have been employed to provide markers for adult human age estimation. Adult age estimation using dental indictors mainly revolves around six indicators: occlusal attrition, secondary dentine formation, cementum buildup, recession of the periodontum, resorption of the calcified tissue surrounding the apical foramen and root dentine translucency (Altini, 1983; Costa, 1986; Kilian, 1989; Saunders, 1965; Xiaohu et al., 1992). Others are mentioned, such as color changes, etching characteristics, microscopic surface changes, loss of water from dentine, dentine hardness (Bang, 1989) and changes in chemical composition of the enamel caused by adsorbed dietary ions (Noble, 1974).

- Secondary dentine: Bodecker (1925, as cited in Costa, 1986) noted that the deposition of secondary dentine in the pulpal chamber grew with increasing age. Gustafson (1950) used the apposition of secondary dentine as one of his six criteria for age determination. Johanson (1971) thought that deposition of secondary dentine on the walls of the pulpal chamber was more related to age, while the deposition on that part of the pulpal chamber closest to the occlusal surfaces is partially a response to attrition process.Therefore secondary dentine deposition of pathological origin is only loosely correlated with age, because there is high interindividual variability (Johanson, 1971) (Fig 2.2).

Fig 2.2 A comparison between teeth belonging to old and young individuals, the decrease in the size of the pulp is due to deposition of secondary dentine (Tencate, 1998)

Abbildung in dieser Leseprobe nicht enthalten

- Cementum buildup: Zander and Hurzeler (1958) studied sectioned teeth from fifty four people under twenty, and seventy teeth from those in the fifty one to seventy six year age group, and found that the cementum thickness tripled. Attempts by Zander and Hurzeler to resolve this more closely found a large interindividual variability, and this was confirmed by Johanson (1971), who found a low correlation with age. Stott et al., (1982: as cited in Hillson, 1986 b) found that in hibernating mammals cementum is laid down in annual rings (Fig 3.3). They examined this possibility for three human cadavers, achieving an accuracy of no more than four years deviation from the true age. Hillson 1986b points out that although it offers potential, the variable quality of preservation of cementum is likely to obscure fine details such as cementum rings (Fig 2.3).

Fig 2.3 Annulations shown in cementum as an age change (Wittwer-Backofen et al., 2004).

Abbildung in dieser Leseprobe nicht enthalten

- Recession of the gingiva: In teeth with healthy attachment to the alveolar bone the gingiva will be attached at a point near the cementoenamel junction. As age increases then the likelihood of an individual having had an episode of inflammation of the gingiva, leading to a minor peridontosis. Hence the extent of the gingival tissue in contact with the alveolar bone will travel down the tooth with increasing age (Johanson 1971). Costa (1986) concluded that this is population specific. Again Johanson (1971) found that there was a very poor correlation between recession of the gingival and age.(Fig 2.4)

Fig 2.4 Ginvigal ressecion with advancement in age, note the exposure of a progressive area
of the dental root and loss of bone and periodontal support (Tencate, 1998).

Abbildung in dieser Leseprobe nicht enthalten

- Root resorption: Resorption of the roots in deciduous teeth during formation of permanent teeth is part of the dental development process. Resorption of the apical end of the root in permanent teeth is considered pathological (Costa, 1986). There is some debate about the sequence of resorption, Johanson (1971) stated that resorption starts from discrete areas on the cement and can be seen as pitting, even on young individuals; on older individuals the number of resorbed areas increases, as does the amount of material resorbed. Costa (1986) concluded that resorption rarely occurs below the age of fifty, thus is only useful in older individuals. Both are agreed that there is a poor correlation with age, and difficulties in measurement of resorbed root.
- Attrition: Once a tooth has reached the occlusal plane it begins to wear out. The normal processes of mastication of food especially that contaminated with abrasives wear the enamel down, then the dentine. Once this stage is reached the rate of wear will increase. Brothwell (1981) devised an ageing method based on the pattern of dentine exposure on the cusps of molars based upon his work on Neolithic and Medieval skeletal material from Britain. This method had the drawbacks that it used age groups that each span ten years, and its upper age limit was forty-five.

One of the problems with dental attrition aging technique is its population specific nature as the rate of attrition depends mainly on the type of food that population consume. In order to overcome this problem Miles (1963) used Anglo-Saxon skeletons to build up an internally calibrated series for that population. The author assumes a period of six years between the first and second molars coming into the occlusal plane. By noting the difference in the attrition between the two teeth, Miles was able to estimate age by examination of the third molar which comes into occlusion some six years after the second. Hillson (1986) points to some criticisms of Miles' method, namely that there is variability in the ages at which various teeth come into the occlusal plane, and secondly the assumptions of uniform wear between different teeth (Hillson 1986 b).

Fig 2.5 Dental attrition and dental attrition scoring model for age estimation

( Brothwell, 1981)

Abbildung in dieser Leseprobe nicht enthalten

- Transparency: Transparency is a change in the macrostructural detail of the dentine. The matrix of dentine comprises of a structure of highly calcified tissue. The major gross macrostructural element of dentinal matrix is the tubule, which contains the odontoblastic cells. These run radially from the dental pulp to the enamel dentine junction (Jenkins, 1966). As an individual grows older calcium salts are deposited into the tubules nearest to the root, causing those tubules to have the same refractive index as the material surrounding them (peritubular dentine), hence they become transparent to (Nalbandian et al., 1960). The outcome of this process is the presence of a transparent zone of dentine running from the apex of the tooth upwards. A condition that increases gradually in extent with advancing age. It has been reported that there is high correlation between the extent of root transparency and age (Costa, 1966; Johanson, 1971) (Fig 2.6).

Fig 2.6 Ground section in a premolar showing progressive root dentine translucency

(Tencate, 1998).

Abbildung in dieser Leseprobe nicht enthalten

Other changes in teeth include: discoloration, teeth tend to turn more yellow, or brown, with age. The hardness and density of all three main dental tissues increases with age. Kani (1954) measured the specific gravity of teeth and found that it increased slightly with age. Kato (1956) found that teeth became more brittle with age. This effect concurs with the generally more heavily mineralized aspect of older teeth. There is also an accumulation of heavy metals in the dentine which could be related to age.

2.3 Root dentine translucency in age estimation

2.3.1 Ontogenyand Physiology of Root Dentine Translucency

Late in the bell stage of tooth development, the cells of the dental papilla adjacent to the internal dental epithelia differentiate to form odontoblasts, which will form the coronal dentine. The root dentine begins to form upon the disintegration of Hertwig’s sheath. Odontoblasts lay down the dentine’s organic matrix of collagen and ground substance, as the collagen fibers are secreted they increase in diameter until the ground substance between them is obliterated. The odontoblasts move towards the center of the papilla and the odontoblast process begins to form. The mineralized matrix begins to form as hydroxyapatite crystals are deposited. (Fig 2.7) (Ten cate, 1998)

Fig 2.7 Late bell Stage and structure of dentine (Ten cate, 1998)

Abbildung in dieser Leseprobe nicht enthalten

Calcium and phosphorous ions are present in the cytoplasm of the tissue. Calcium channels on the dentinal cell membranes are activated by the production of ALP (alkaline phosphatase) and CaATPase. Mineralization proceeds by “globular calcification” by which crystals are deposited in discrete areas, which are enlarged until they eventually fuse. Dentine mineralizes incrementally at a rate of approximately 4 um per day and small shifts in the orientation of the fibers are visible at these increments. Greater changes in orientation occur on a five day cycle (approximately 20 um apart) and are known as von Ebner’s lines. The rate of deposition for root dentine is slower, the orientation of collagen is different from that of the coronal dentine, and this dentine is mineralized to a lesser degree (Ten cate, 1998).

2.3.2 Review of the Literature on Age Estimation using root dentine translucency

Paultauf was the first to describe the phenomenon of dental transparency in 1903 (Marcsik et al., 1992) and this feature has been used to estimate age-at- death for nearly a century (Sengupta et al., 1998). A direct relationship was discovered between chronological age and amount of transparency; as age increases, the amount of transparency in the tooth root also increases (Gustafson, 1950; Marcsik et al., 1992; Hillison, 1996). The forensic pathologist, Professor Lacassagne, was the first to utilize apical translucency as an indicator of chronological age in 1889 (Johanson, 1971; Wilson,1989; Russell, 1996).

Translucency of the root can be analyzed in longitudinal thin sections (Gustafson, 1947, 1950, 1955; Dechaume et al., 1960; Nalbandian et al., 1960; Johanson, 1971; Solheim and Sundnes, 1980; Vasiliadis et al., 1983; Whittaker and Bakri, 1996; Sengupta et al., 1998, 1999) or on intact teeth (Bang and Ramm, 1970; Colonna et al., 1984; Solheim ,1989; Drusini et al., 1991; Lamendin et al., 1992; Prince and Ubelaker, 2002; Sarajliæ et al., 2003) (Fig 2.11).

Translucency of the root can be seen macroscopically, but is enhanced with the aid of a light box (Fig 2.8). There are several advantages of taking measurements directly from intact teeth: it is non-destructive, less expensive and less time consuming than other methods and it is not necessary to have a complete knowledge of dental histology.

Fig 2.8 Different methods of assessing root dentine translucency as described in literature
(intact, with the aid of light box, half sectioned, ground sectioned).

Abbildung in dieser Leseprobe nicht enthalten

Several researchers have found a significant difference between the sexes (Lorentsen and Solheim, 1989; Prince and Ubelaker, 2002), while others have not (Drusini et al., 1991; Lamendin et al., 1992). Lorentsen and Solheim (1989) suggested that sexual dimorphism in translucency may be attributed to differences in masticatory forces. Similarly, ancestry variation has been noted by several authors (Whittaker and Bakri, 1996; Prince and Ubelaker, 2002) .

Gustafson (1950) was the first to note the morphological changes in the structure of teeth with age . These were attrition, periodontosis, secondary dentition, cementum apposition, root translucency and root resorption. He awarded a score of 0-3 based upon visual severity of changes and estimated age (Fig 2.9). He calculated age using the regression formula derived from his observation: Y = 3.52 X + 8.88 (X = Total Score and Y = Estimated Age).

Gustafson established that the difference between calculated age and real age would not exceed +/-3.6 years in 33% of cases, +/- 7.3 years in 4.5%cases, +/- 9.1 years in 1% of cases and +/-10.9 years in 0.3% cases.

Fig 2.9 Scoring of different morphological changes in the structure of the tooth according to Gustafson 1950. (Gustafson, 1950)

Abbildung in dieser Leseprobe nicht enthalten

Although the importance of Gustafson’s research was evident, many authors (Dalitz, 1962; Saunders, 1965; Bang and Ramm, 1970; Burns and Maples, 1976; Johanson, 1971; Maples, 1978; Maples and Rice, 1979; Metzger et al., 1980 ; Solheim and Sundnes, 1980; Haertig et al., 1985; Nkhumeleni et al. , 1989; Kashyap and Koteswara Rao, 1990; Marcsik et al., 1992; Lamendin et al. , 1992; Solheim, 1993; Borrman et al., 1995; Lucy and Pollard, 1995; Lucy et al., 1996; Aykroyd et al., 1997; Ubelaker et al., 1998; Baccino et al., 1999; Monzavi et al., 2003) noted problems with his analysis, in particular, the statistical methodology, and tried to improve upon his foundation

Dalitz (1962) was the first to offer a modified method based on Gustafson’s dental features. He analyzed 128 incisors and canines extracted from 29 cadavers. His modifications included adding an extra phase at the latter end of the scale, therefore scoring dental changes from 0 to 4, and omitting cementum apposition and root resorption due to their low correlation with age. Age was estimated using multiple regression analysis, which weighted the remaining four dental changes. From this modified approach, a mean error of 8.1 years was produced Johanson (1968) was the first to attempt to estimate age by using root dentine translucency as an ordinal variable. Johnson used a sample of ninety- three anterior teeth, from twenty-seven males, aged between 28 and 73. He measured ratios of transparent dentine to opaque dentine area, total root area, total root dentine area, and the width of the root at the cemento-enamel junction. There was found no significant correlation between any of these ratios and age.

Bang and Ramm (1970) measured the minimum and maximum length of the translucent dentine in 400um labial-lingual longitudinal sections from a sample of 1402 teeth taken from some 265 individuals, from a modern Scandinavian population. They found correlation coefficients between 0.90 (maxillary second left pre-molars) and 0.5 (mandibular molars, 1st root), correlation coefficients being about 0.65 for most teeth. Bang and Ramm found no sex dependency.

Johanson (1971) analyzed 162 teeth extracted from 46 individuals. Johanson also increased the number of ordinal phases in his modified method by adding intermediate stages of dental change, thus offering a method based on 7 phases instead of 4 ( Fig 2.10). He also used multiple regression with weighted coefficients, as Dalitz (1962) did, to estimate age-at-death. From his method, a mean error of 5.16 years was attained .

Fig 2.10 Scoring of different morphological changes in the structure of the tooth according to Johanson 1970.( Johanson, 1970)

Abbildung in dieser Leseprobe nicht enthalten

Pillai and Bhaskar (1974) studied 83 anterior teeth collected from 59 cases (36 males and 23 females) and recorded physiological changes in tooth with age and comparison of these changes in males to that in females. Score was calculated and they plotted graph of known age versus score and regression equation was deduced Y= 5.34 X - 4.08. They also found that the six factors used by Gustafson were age related variable but there was no significant relation with the sex of the person. They found that chewing habits exposed the teeth and surrounding tissue for degenerative changes thus giving rise to higher point value. Dark stains due to pan and tobacco tended to be more on gingival surface stimulating degenerative changes earlier although study proved that whether vegetarian or non-vegetarian doesn’t seem to influence the age changes in and around teeth.

Maples (1978) offered an improved method that reduced the number of dental variables. Maples analyzed 355 teeth from dental extractions, of which 284 comprised the working sample and 71 the control sample. Maples tested each of Gustafson’s dental features individually as well as in combination with the other features. His results yielded standard errors 20-30% lower than Gustafson, in most cases. M2 provided the best results with APSCT (Attrition,

Periodontitis, Secondary Dentin, Cementum, Transparency) and yielded a mean error of +5.00 years. His results revealed that root resorption was by far the worst of the six changes while root transparency was the best, followed by secondary dentin, attrition, periodontitis and cementum. Secondary dentin deposits and translucency of the root were the best predictors of age. In addition to having higher correlations with chronological age, Maples found that they were the easiest features to assess and less prone to pathological and taphonomic processes. Maples stated that these two features can be utilized to estimate age- at-death in contemporary and archaeological material. Furthermore, there was no significant difference among ancestry groups or between the sexes.

Maples and Rice (1979) found that although Gustafson’s method was a significant contribution to forensic identification but many statistical errors were present in the published articles. It was improved and new formula was found using multiple regression techniques. Formula derived was Y = 4.26 X + 13.45 (X = Total Score and Y = Estimated Age), (r = 0.912) and value of error as +/-

7.3 years

Lamendin and Cambray (1980) measured the length of root dentine transparency from a sample of 217 teeth. They found a correlation coefficient of 0.73 with age, although they did not calculate separate age estimation equations for each tooth type. Likewise, Wegener and Albrecht (1980) measured root dentine transparency for a sample of 601 teeth from 50 individuals. They did not treat each tooth type separately, but still obtained a correlation coefficient of 0.67.

Metzger and colleagues (1980) suggested that sections 250 um through the center of the root would bypass large areas of translucency. they suggested that the sections be made 1 mm thick.

Solheim and Sundnes (1980) compared the age estimates obtained from traditional macroscopic observations with those obtained using the intact tooth method of observing root translucency (Bang and Ramm 1970) and the Gustafson method (Dalitz 1963; Miles 1963; Johanson 1971). They found that Johanson’s (1971) method was the most accurate among the histological techniques, and compared most closely with traditional macroscopic estimates coming within one or two years. The standard error was calculated at 10 years, much less than the error for the Bang and Ramm (1970) method. The authors found no significant differences in accuracy of estimates from pathological specimens, by sex or tooth class.

Whittaker (1982) reviewed various method of age estimation and found that beyond young adulthood the age estimation becomes difficult. They found that Gustafson’s method was quite satisfactory in estimation. It was suggested that most sensitive indicator of age estimation is the degree of development of translucent dentine at the apex.

Drusini and colleagues (1989) tested the macroscopic method of evaluating dentine translucency developed by Bang and Ramm (1970). The teeth were examined under a tungsten light and sliding calipers were used to measure the zone of transparency. The length of the transparent zone was not symmetrical on two sides of the root so an average was calculated. An average was also calculated for all sides of the roots in multi rooted teeth. They tested the method on 382 teeth (32 anterior, 33 premolars, 81 molars) from 311 individuals of known age and sex. The samples were from both living people and 100 of the individuals were from a skeletal population. The root translucency was measured, multiplied by 100 and then divided by the total root length. Their standard error was comparable to that of Lamendin (1992) in 50% of observations (+/10 years). However, 50% of the measurements had a standard error of greater than 20 years. They found the method showed the best correlation with known age at death in second molars (r = 91%).

Lorentsen and Solheim (1989) examined relationship between age of the person and area of translucent dentine at the root apex and compared with the methods of Johanson and Bang & Ramm. They used 500 teeth in their study, 50 of each tooth type, except molars and analyzed them under XT microscope and SPSS/PC regression programme was used. Regression analysis using several factors according to Johanson’s method resulted in stronger correlation for most of the teeth, than that of Bang & Ramm method.

LopezNicolas et al., (1990) measured transparency, among other variables, in 1 mm sections. Their age estimates had a prediction error of 2.05 years with a confidence interval of 95%. However, 1mm thick sections have been criticized as difficult to examine due to overlapping information. This danger is supported by studies of half sectioned teeth that have produced low correlations between known age and estimates, ranging from 31-72%. (Solheim 1989, 1990, 1993; Lorentsen and Solheim 1989).

Kashyap and Rao (1990) conducted a study designed to minimize the difficulties in quantification of attrition, secondary dentine, translucency and cementum annulations. They collected 25 teeth from cadavers in Hyderabad, India. The individuals were between 18-45years of age. The authors applied the Gustafson (1950) technique to their sample and obtained an error in estimation of +/8.13years. The authors also developed their own method of age estimation using indices for four age related changes. Their index for attrition was the width of the worn area divided by the width of the tooth at the CEJ. The index for secondary dentine deposition was the length of the secondary dentine divided by the length of the entire pulp cavity. Translucency was indexed by dividing the length of the translucent area by the length of the entire tooth. The thickness of cementum was measured at the thickest point and divided by the width of the tooth at that point. The idea was to create indices for each measurement, to make the technique more specific to individual variation. The method was highly accurate and precise in their study. The index values showed a linear relationship with known age at death. The cementum index required a square root transformation. Their estimates, derived from the mean of the four indices, were very highly correlated with known age at death (r = 0.998). The standard error was 1.59 years, a smaller number than any produced thus far by any other study. The precision of the technique warrants further testing of this method on samples of known age, particularly given that the original sample size was 25 individuals.

Drusini et al., (1990) tested the idea that root dentine translucency shows the best correlation with age of all the criteria in the Gustafson (1950) method. The authors looked at 70 teeth (33 premolars, 37 molars) from 46 adults of known age and sex. They sectioned one root from each tooth to 600 um thickness (in the buccolingual plane). The authors used a light microscope at 6 times magnification and measured total root length as well as root transparency. Two observers took the measurements and there was no significant difference between the two scores. The authors noted a tendency to overestimate age in young individuals and to underestimate in older individuals. The margin of error around their estimates was +/5 years in 21% of cases, +/10 years in 26% of cases, and the other half of the estimates had a margin of error between 10-20 years. The best correlation between the estimate and known age was 58 %. The authors attribute their low success rate to the difficulties of getting the full zone of transparency included in the section. The average correlation for the premolars was 49%, for the molars it was slightly higher at 55%.

Drusini et al., (1991) based their method on that of Lamendin (Lamendin and Cambray 1981). The authors created four indices 1.) translucent area/length of translucent zone; 2.) root area/translucent area; 3.) translucent area x length of translucent zone; and 4.) translucent area x translucent zone. They made their measurements on 366 intact teeth from clinical extractions and buried remains 100 years old. The correlation between known age and estimated age was 86% with a standard error of 7.10 years.

Drusini et al., (1992) measured root dentine translucency for 152 teeth from 134 individuals by using light transmitted through the tooth root by a high intensity light. They measured both the length of the sclerotic zone and the area, which were both expressed in terms of their relative proportion of the total root length and area, respectively. It was found that the correlation with age was 0.58 for anterior teeth, and 0.84 for sclerotic length on premolars. Sclerotic area for pre-molars had a correlation of 0.81 with age.

Lamendin et al., (1992) proposed a technique to study single rooted tooth. It is based on measurement of two dental features: periodontosis height times 100/root height (P) and Transparency of the root height times 100/ root height (T)(Fig 2.11). Their sample consisted of 135 males, 73 females, 198 whites and 10 blacks. The sample ranged from 22 years to 90 years. By using formula (A =0.18 x P + 0.42 x T + 25.53, where A =Age in years, P = Periodontosis X 100/root height and T= Translucency height X 100/root height), they were able to calculate the age at death with an error between the actual age and calculated age, of +/- 10 years on their working sample and +/-8 years on a forensic control sample. A comparison of the Gustafson’s method and Lamendin methods on the control sample of 39 teeth resulted in the advantage of the latter considering the mean error on the estimation (14.2 +/- 3.4 years versus 8.9 +/- 2.2 for Lamendin).

Fig 2.11 Measurements proposed by Lamendin for age estimation, root height RH, periodontal regression P and translucency of the root RDT (Lamendin et al., 1992)

Abbildung in dieser Leseprobe nicht enthalten

Lamendin et al., (1993) used root dentine translucency measured by light transmitted through the tooth root in conjunction with an estimate of periodontal recession. It was found that age estimates coincided with those from other anthropological age estimates for a sample of 91 individuals from a churchyard in Oslo, but, as the individuals were not of known age it was not possible to judge how effective the two combined dental criteria were.

Solheim (1989, 1990, and 1993) conducted a series of tests using the Gustafson (1950) and Johanson (1971) criteria on 1000 teeth, 100 from each tooth class except for the molars. Johanson (1971) had found that root dentine translucency was the single variable that was most correlated with age at death (r = 0.84). In a test of root dentine translucency alone, the authors measured the length of the translucent zone for intact and half sectioned teeth (1989). They found the sectioned teeth to be more useful, as the measurements made on intact teeth had large interobserver error. The sectioned teeth showed little variation between tooth class and the influence of pathological conditions was negligible. The same sample was used to test the correlation of secondary dentine deposition, the width of the pulp chamber, and known age (1993). The authors only found a 60% correlation using these two criteria.

López-Nicolás et al., (1993) tested the properties associated with apical translucency using IBAS image analysis. Their goal was to examine the number of dentin tubules and the tubule diameters to determine their applicability in estimation of age-at-death. The researchers cut longitudinal thin sections which were 1mm in thickness. Sections were then cut transversely from the CEJ to the apex of the root, which were approximately 0.25mm to 0.50mm thick to assess the dentin tubules. The number of tubules and their corresponding diameters were measured under 2000X magnification. Their results yielded a significant correlation between the number of tubules and chronological age, r= -0.2046. Their results also yielded a significant correlation between the number of dentin tubules and the maximum tubule diameter, r= -0.3246. Although this analysis provided significant correlations, they are very weak and would probably not be useful in age estimation.

Kwak and Kim (1993) studied 157 extracted teeth, 73 of the teeth originated from males and 84 from females from age groups 12 to 79 years. The correlation coefficient of each Gustafson’s criteria in relation to age was carried. Age estimation were performed on 157 teeth according to the method by Gustafson’s method and by use of multiple regression, as used by Johanson, alter evaluating the six criteria of Gustafson by multiple regression computer analysis. Two prediction formulas and standard deviations were compared with each other. They found that the six Gustafson’s criteria had strong correlation with age except root resorption and correlation coefficient were r = 0.79 (transparent dentine), r = 0.72 (secondary dentine), r = 0.69 (peridontal change), r = 0.63 (Attrition), r = 0.39 (root resorption).

Kvaal and Solheim (1994) studied age related changes in 452 extracted, un-sectioned incisor, canines and premolars. The length of the apical translucent zone and extent of the periodontal retraction were measured on the teeth while pulp length and width as well as root length and width were measured on the radiographs and the ratios between the root and pulp measurements calculated. For all types of teeth significant, negative Pearson’s correlation coefficients were found between the age and the ratios between the pulp and the root width. It was also found that there is correlation between age and length of apical translucent zone but the correlation was weaker than expected while the correlation of age with peridontal retraction was significant in maxillary premolars. The correlation coefficient ranged between r = 0.48 to r = 0.90 between chronological age and calculated age, using formulae from this multiple regression study. The strongest correlation coefficient was found for the premolars.

Baccino et al., (1998) compared four single indicator methods, which were single-rooted teeth (Lamendin et al. 1992), 4th sternal rib ends (Ÿ^can et al. 1984a, 1984b, 1985), the pubic symphysis (Brooks and Suchey 1990), and femoral cortical bone remodeling (Kerley 1965, Kerley and Ubelaker 1978). They also compared three multifactoral methods, which included the Average method (Baccino et al., 1999), the Global method (Baccino et al., 1999) and the Two-Step method (Baccino and Zerilli 1997).The techniques were applied to 19 adult individuals, 15 males and 4 females, who ranged in age-at-death from 19­54 years, with a mean of 37.6 years and a standard deviation of 10.0 years. All individuals had a European (French) ancestry. Two observers performed each of the seven methods on the 19 individuals and a third observer performed only Lamendin’s technique on the sample. Both observers who tested all seven methods. They found that the Lamendin method was the best single technique, with negligible intra and interobserver error, however the method was most accurate when combined with the other techniques.

Sengupta et al., (1998) found that intra and interobserver error were not significant in measurements of sectioned teeth; interobserver error was significant for intact teeth. The canine was the preferred tooth class for sectioning because the roots are generally broad and straight. The authors also found that the stains that they tested did not improve resolution of the translucent area so teeth were examined without the aid of stain.

Valenzuela et al., (2002) studied two different populations forty-three permanent teeth (Group I), extracted for valid clinical reasons, were taken from patients 25-79 years of age. The other population group (Group II) was composed of 37 healthy erupted permanent teeth obtained from human skeletal remains (age 22-82 years) with a postmortem interval ranging from 21 to 37 years. The aim of the study was to (1). Measured parameters that contribute significantly to estimates of dental age, using a combination of classic methods and a computer-assisted image analysis procedure to avoid the bias inherent in observer subjectivity; and (2). Development of new mathematical regression models for age prediction according to postmortem interval. Morphologic age- related changes were investigated by measuring variables on intact and half- sectioned teeth. Multiple regression analyses were performed with age as the dependent variable for each sample source. They found that in fresh extracted teeth, the variables that made the greatest contributions to predictions of age were dental attrition, dentin color, and translucency width; the latter measured with a computer-assisted image analysis method. They also found that in teeth from human skeletal remains, the variables that made the greatest contributions to age calculation were cementum apposition, pulp length measured by computer-assisted image analysis, dental attrition, root translucency, and dental color. Thus they recommended use of different regression models to calculate age depending on the postmortem interval.

Prince and Ubelaker (2002) analyzed 400 single-rooted teeth extracted from 355 individuals from the Terry Anatomical Collection, housed at the Smithsonian’s National Museum of Natural History. A mean absolute error of 8.23 years, with a standard deviation of 6.87 years was produced employing Lamendin’s method and formula. To further assess the accuracy of this method, Prince and Ubelaker (2002) analyzed the mean error of age cohorts, broken into 10 year segments. Lamendin’s method was found to be the most accurate for the 30-69 year old age groups, which holds true for the original Lamendin study and the Terry Collection sample. Once outside this range, below 30 and above 70, mean errors increase greatly. Applying Lamendin’s technique to the Terry Collection produced the typical aging bias , where older individuals were underestimated in age, while younger individuals were overestimated in age. The authors created new formulae separating individuals by sex and ancestry and included root height, which significantly lowered the mean errors.

Morzavi et al., (2003) studied 210 teeth ranging between 25 to 60 years including 185 males and 25 females. By using 0.5 - 1 mm section of tooth, determined attrition, periodontitis, root resorption, secondary dentine deposition, cementum apposition and root translucency. They found that among different mandibular teeth, the sum ranks of the first premolars factors had best correlation with the age and also that the sum of the dental factors presented a better model than each of the factors alone. Correlation coefficient of age with attrition, periodontitis, root resorption, secondary dentine, cementum apposition and root translucency was found 0.394 (p < 0.001), 0.384 (p < 0.001), 0.169 (p < 0.014), 0.522 (p < 0.001), 0.251 (p < 0.001) and 0.344 (p < 0.001), respectively

Saglam Atsu et al., (2006) examined 21 single root teeth, which were extracted from individuals with known age in the range of 13-70. Intact teeth without taking any cross-sections were studied under a light microscope ad­justed to x 10 magnification, with a ruler having 0.001 mm grades, and digitally recorded (Fig 2.15). The photos were transferred to computer in JPEG format, and then length and area measurements were done by Photoshop 7.0 image analyzing programme. Different regression models were examined, to explain the relation between age and root transparency. Cubic regression model was used to examine the relation between age and root transparency, which was found to be a suitable model for the data (a= 0.05). The results of the regression analysis showed that, a statistically significant relation between age and the root transparency exists (p< 0.001) and it was concluded that age estimation from teeth can be performed from the ratio of length of root transparency to total root length (r2= 0.88), with ± 4.9 age error, by a regression formula as Age= 7.409 + 2.77X-0.05X2 + 0.001X3, and from the ratio of root transparency area measurement to total root area (r2= 0.86), with ± 5.5 age error, by a regression formula as Age= 13.565 + 3.57X-0.08X2 + 0.001X3 as well.

Fig 2.12 Intact teeth under a light microscope adjusted to x 10 magnification, with a ruler having 0.001 mm grades according to Saglam Atsu et al., (2006)

Abbildung in dieser Leseprobe nicht enthalten

2.3.3 Root dentine translucency in archaeological contexts

Except for dental attrition, there have only been a small number of analyses of dental aging methods applied to archaeological material (Sengupta et al., 1999). Research analyzing translucency of the root and periodontal recession resulted in conflicting conclusions of their usefulness and applicability of estimating age-at-death in archaeological samples. Some researchers concluded that apical translucency and periodontal recession were extremely hard to determine in archaeological samples (Vlèek and Mrklas 1975, Marcsik et al. 1992, Sengupta et al. 1999) owing to soil apposition in the tooth root, preservation issues of the tooth and decomposition of the gingiva. Other researchers concluded that translucency of the root was a good indicator to estimate age at- death and would be a useful indicator to estimate age-at-death in both contemporary and archaeological samples (Acsádi and Nemeskéri 1970, Maples 1978, Colonna et al. 1984, Drusini et al. 1991).

Root dentine translucency has been applied to skeletons of unknown age from archaeological contexts. As with any aging indicator, taphonomic processes may affect the properties and visual assessment of apical translucency. These processes include water insults, soil conditions, temperature and humidity, and faunal, fungal, or bacterial scavenger activity (Sengupta et al. 1999). Archaeological samples present additional challenges. Heavy amounts of post depositional mineralization and damage to the root surface could make observations on intact prehistoric teeth difficult. In addition, archaeologically derived teeth tend to fracture and fragment upon sectioning. It is unknown how much the standard error increases for age estimates based on methods developed from modern forensic reference samples and used on teeth derived from different temporal and geographical contexts. Periodontitis cannot be accurately measured on teeth when soft tissue has decomposed, so multivariate methods employing measures of periodontal disease in addition to translucency are less useful.

Drusini et al., (1991) analyzed modern and historic teeth in order to address if translucency of the root was applicable to buried historic samples, and if regression formulae developed from modern samples were suitable to estimate age-at-death for historic samples. They tested two methods of measuring translucency of the root: direct measurement with vernier calipers and measurement made with an IBAS 2000 computerized densitometric analyzer. Their sample contained 152 single-rooted teeth of known-age and sex comprised from two sub-samples. Their modern sample contained 86 single-rooted teeth, 50 anterior teeth and 36 premolars, and their historic sample contained 66 single-rooted teeth, 33 anterior teeth and 33 premolars. The historic sample was obtained from individuals who were buried in Italy between 1890 and 1930. They measured the maximum apical translucency (h) and the root height (H) of each tooth with the vernier calipers and the IBAS system. In the latter, black and white photographs were taken and measurements were made from the photographs. After calibration was complete with the IBAS system, measurements were made semi-automatically.

They expressed the translucency of the root as a proportional index: h*100/H and regressed that index against age. A regression formula was generated for both measurement methods. The regression formulae were tested on three control samples, which contained 14 modern anterior teeth, 33 historic anterior teeth, and 33 historic premolars. From their control samples, the premolars yielded the highest correlation coefficients between age and proportion of apical translucency, independent of sample and which measuring methods were applied, r = 0.84 for calipers and r = 0.81 for IBAS. With the historic sample premolars, 48.49% of the measurements with calipers and 45.46% measured with the IBAS produced ages ±5 years, Drusini et al. (1991) state that utilizing translucency of the root to estimate age-at-death in samples buried for approximately 100 years is a reliable technique.

Marcsik et al., (1992) analyzed 200 mandibular incisors from the 8th century and 50 polished sections of mandibular incisors from the 8th and 10th centuries to assess if translucency of the root was applicable in estimating age- at-death for archaeological samples. They compared dental age with skeletal age which was estimated from the pubic symphysis, epyphyseal closure, and endocranial suture closure (after Acsádi and Nemeskéri 1970). All individuals in the sample were adult. Regression equations from Miles (1963) and Bang and Ramm (1970) were utilized to estimate age-at-death from observed apical translucency. Dental age consistently yielded higher age estimates than the skeletal age, particularly with Bang and Ramm’s (1970) formula. In 36% of their cases, no translucency of the root was observed, which was attributed to soil conditions.

Bang (1993) used root dentine transparency to estimate the age of a prehistoric individual found in Norway using 400 um longitudinal labial-lingual sections of a left canine and left pre-molar from the mandible, and right canine from the maxilla. The age of the individual was estimated to be about 60 years, and the date taken from associated pollen and uncalibrated radiocarbon measurements was about 5000 BC.

Beyer-Olsen et al., (1994) estimated the age of 248 skeletons excavated from the Medieval church of St. Olaf's in Trondheim, Norway, using 400um labial-lingual longitudinal sections. The estimated ages were not significantly different to those obtained by more conventional anthropological means. However, Lucy et al. (1995), when attempting to use root dentine translucency to estimate the ages of four individuals, from eight sections of six teeth, from the Medieval cemetry of the hospital of St James and St Mary Magdalene, Chichester (Lee and Magilton, 1989; Magilton and Lee 1989), found identifiable root dentine translucency in only one individual. This individual was a 50 year old male aged from sections of a right maxillary second molar, and a left maxillary central incisor. All the other seven teeth inspected had suffered extensive tunnelling by fungi (Dye et al., 1995; Lucy et al., 1995), suggesting that not all individuals from the archaeological record possessed teeth from which age estimates could be made by observations of root dentine translucency.

Lucy et al., (1995) analyzed modern and archaeological teeth to assess the applicability of Gustafson’s six dental features to archaeological material. Although they utilized Gustafson’s six features, they used modifications of his method to carry out analyses. They followed Johanson’s (1971) method of assessing the degree of dental change, except for apical translucency, where they followed the method outlined by Bang and Ramm (1970), who took direct measurements of translucency. Estimated ages were made from Johanson’s (1971) formula, Bang and Ramm’s (1970) formula, and Maples and Rice’s (1979) modified Gustafson formula. They analyzed a sub-sample of the modern extracted teeth, which consisted of 24 teeth from 17 individuals. Longitudinal thin sections (300 um) were taken through the center of the roots and multiple- rooted teeth were sectioned through each root. A total of 35 thin sections was assessed for amount of dental change with each section being treated as a separate individual. Another study was conducted to assess differences in the same tooth with multiple roots, as well as different teeth from the same individual. In order to assess how well each formula fared they compared the average absolute deviations (the average difference between the estimated age and actual age) from each formula and compared the quoted standard errors with the ones produced in this study.

Their results showed that Johanson’s (1971) method was slightly better than Maples and Rice (1979) and Bang and Ramm’s (1970), with average absolute deviations of 4.5 years, 5.03 years, and 5.15 years respectively. The authors then assessed a very small sample of 8 teeth from 4 skeletons from the Medieval Hospital cemetery at Chichester. One incisor and one molar from each skeleton were analyzed. Dental age was correlated with skeletal age, where skeletal age was estimated from the pubic symphysis, epiphyseal closure, M3 eruption, and sternal rib ends (all as described in Bass 1987).

Sengupta et al., 1998 tested the applicability of root dentine translucency studies for archaeologically derived teeth. They tested several methods on both modern, clinically extracted teeth and on two historic period skeletal assemblages. The teeth were examined intact, following Bang and Ramm (1970) and then embedded. They collected three buccolingual sections 250-150 um thick from the center of each tooth. A test of six stains showed unstained teeth to provide the most resolution. The authors took monochrome digital photographs of each section over a light box with a macrolens. They used Microscale TM/TC program for digital analysis, tracing all of the areas to be measured using the mouse. They measured the length and area of root transparency, the percent length of root translucency (length of translucency divided by the total root length), and the percent area of root translucency (translucent area divided by the total root area).

Sengupta et al., (1999) analyzed the degree of apical translucency in known-age archaeological material and compare its applicability and reliability to known-age modern material. The sample consisted of 56 non-pathological mandibular canines of known-age extracted from dental clinics and forensic cases . 68 non-pathological mandibular canines from a known-age archaeological collection, the Christchurch Spitalfields Collection. The Spitalfields Collection contains individuals from exhumed nineteenth century burials, where records of date of birth, date of death and in some cases occupation were available (Molleson et al. 1993).They cut three buccolingual longitudinal sections, grounded to a thickness of 100 pm. These researchers then categorized the translucency of the root into three groups: measurable root translucency, “chalky” dentin, and unaffected tubular dentin. They expressed the measurable root translucency as a direct measure, a percentage of the total root, area, and area percentage measured with image-analysis. Their results produced a much higher correlation between age and translucency as a percentage of root height in the modern sample as compared with the archaeological sample, 0.73 and 0.52 respectively. In addition, they found no teeth in the modern sample to exhibit “chalky” dentin, while several teeth from the archaeological sample exhibited this feature. These researchers concluded that root translucency should not be utilized to estimate age-at-death for archaeological material, and some forensic material, until more research has been compiled between translucency and taphonomic processes.

2.4 Dentalrootcolorinageestimation.

Morphological, histological and biochemical methods based on degenerative changes in the teeth have been developed to assess age at death. Because of their reliability, morphological changes in teeth form the basis of some of the most common methods to estimate age in forensic cases. Dental color is the most frequently used criterion for formulating multiple regression models in order to assess age at death of the individual.

2.4.1 Review ofthe Literature on Age Estimation using dental root color

Ten Cate et al., (1977) analyzed root color change as an indicator of chronological age. In their study, the color of root dentin was compared to known-aged standards. The amount of change was assessed and the teeth were arranged in 5-year age cohorts. All age estimates were within ± 10 years of actual age. Sex did not yield a significant difference. The authors concluded that this was a useful method, but that training was required to assess the degree of color change. Ten-Cate et al. (1977) found that the roots of teeth tend to get darker later in adult life which is thought to be due to the deposition of blood products in the dentine structure (Solheim, 1988). Ten-Cate et al. found age estimates could be made for unknown specimens by comparison with known specimens grouped by age, although there were some doubts about the accuracy of these estimates. They then went on to measure color on a continuous scale with colorimetery. The reported correlation of root color with age is 0.9, but the authors do admit to some deletion of what were termed 'statistical outliers'; there was no evidence for either the sex of the individual, or any oral pathology affecting the color.

Solheim (1987) used 758 teeth of known age of extraction from a Scandinavian population, and measured the color of the root using a dental color classification system and colorimetery. Using the dental classification system the correlation with age varied between 0.59 (mandibular central incisors) and 0.84 (maxillary central incisors). Colorimetery revealed correlations with age which varied between 0.55 to 0.78. It was concluded that color was more correlated to age than many dental age changes.

Solheim (1988) analyzed 1000 extracted teeth of known-age from Washington and Oregon States, which ranged in age-at-extraction from 14-99 years. All tooth types except molars were represented in the sample, which consisted of 100 teeth from each tooth type, 50 from each side. Teeth were extractions from dental clinics, forensic cases, and anatomy classes. Crown color was estimated by comparing the tooth to a dental shade guide in both a wet and dry state under a fluorescent light. Three different color guides were utilized: Trubyte, Bioform, and Dentsply International. The root was then ground approximately 0.5mm along the longitudinal axis, in order to remove the cementum and to expose the root dentin. The reflected light was measured at the midroot level with the aid of a super Speedmaster reflection densitometer. As with the crown, readings were also taken in a wet and dry state. From multiple regression analysis, correlation coefficients ranging from 0.77 to 0.87 were obtained. In the crown, a 5-grade scale was found to yield the highest correlations with chronological age, except for maxillary canines. The weakest correlation was found between the Trubyte dental shade guide and age, as compared with the other methods of measuring color. The author found that visually ranking dentin color and using the spectophotometer increased the correlation with age, although on an individual basis the visual assessment yielded a higher correlation with age than spectophotometry. He noted that use of yellow reflection, rather than total reflection, improved the correlation with the spectrophotometry. Dry assessment yielded a significantly better correlation than assessment of color in the wet state, independent of which method was being utilized.

There was no significant difference between the right and left sides, between chronological age and tooth age (age minus age at root completion of the tooth), reason for extraction, or between the sexes. There was a weak association between darkness of the tooth and post-mortem versus pre-mortem sampling which was significant for a number of different tooth types. The author noted that several factors caused discoloration, which was different from the color change he was assessing to estimate age. Discoloration was a result of pulp necrosis and tetracycline staining. In addition, a reddish/purple discoloration was noted in deceased individuals. The author concluded that assessment of dental color as an indicator of age is a reliable method.

Lackovic and Wood (2000) assessed root color change in known-age and sex extracted teeth to estimate chronological age. They had three main goals: 1) to evaluate the reliability and applicability of tooth root color change as an indicator of age, 2) to determine if a significant difference existed between anterior (non-molar teeth) and posterior (molar) teeth and surfaces, and 3) to determine if a linear relationship exists between tooth root cyan, magenta, yellow, and black coloration and age. To test these hypotheses, three experiments were conducted. The first experiment analyzed 21 teeth from 2 age cohorts, 20-24 year old females and 70-74 year old females, in which the authors measured 6 points for percentage of yellow saturation. Their results indicated that the mesial surface from the 20-24 year old females was significantly different from the other three surfaces in percentage of yellow saturation, while the mesial surface was significantly less saturated. In the 70-74 year old females, a significant difference was found between all surfaces, except the distal surface. To assess differences between anterior and posterior teeth, 21 teeth, 11 molars and 10 non-molars, were analyzed from the 20-24 year old females.

The results produced a significant difference on the buccal-lingual surfaces between the molar and non-molar teeth, therefore yielding a significant difference between anterior and posterior teeth. In addition, 40 teeth, 20 molars and 20 non-molars representing both sexes, were analyzed from each 5-year age cohort, starting at 15-19 year olds through 80-84 year olds. Four points were assessed on the teeth to assess the amount of color change. The results from the third test yielded a positive increase in the percent of measured color with age. The highest correlation for males and females was cyan and chronological age, r = 0.93, with the next highest being magenta, r = 0.93 and 0.81 for males and females, respectively. Lackovic and Wood (2000) point out several advantages of this aging method. This method does not require tedious lab techniques - it can be performed with minimal dental anatomy knowledge, and it is a non­destructive and inexpensive method. Some disadvantages include that the teeth must be extracted and taphonomic conditions may influence the coloration of the tooth root.

Martin-de las Heras et al., (2003) analyzed dentine color using spectroradiometry in 250 teeth from patients ranging in age from 10 to 89 years. Color measurements were performed as suggested in the CIE 1931 (International Commission on Illumination). Values for chromaticity coordinates (x, y, z), luminance (Y), whiteness index (WI) and yellowness index (YI) were obtained. Correlations between these colorimetric variables and aging were established by linear regression analyses. All the variables fit the mathematical model with correlation coefficients ranging from 0.53 to 0.75. This method of color

measurement produced an expected associated error of calibration averaging 13.7 years about the mean estimated values, at a 70% level of confidence. Multiple regression models for dental age estimation were tested, it was concluded that determination of dentine color by spectroradiometry is an objective method to estimate age in forensic studies.

Laškarin et al., (2006) examined the relationship between tooth root colour and age, and its possible application in age assessment. In this research the authors analyzed 100 tooth roots. All teeth were digitally recorded and the colorimetric processing was made using Adobe Photoshop 7.0® computer program. Studies have shown no significant difference between RGB values analyzed on the whole root surface or only on its central part, with certainty p>0.99. It was also established that there is no statistically significant difference in coloration on the four anatomical surfaces (buccal, mesial, lingual, distal) of tooth roots with certainty p>0.99 for red, p>0.99 for green and p>0.50 for blue color component. Statistical data interpretation showed that there is a linear correlation between obtained red ,green ,and blue (RGB) values and age, with r= -0.994, p>0.99 for the red component, r= -0.972, p>0.99 for the green and r= - 0.982, p>0.95 for the blue color component.

Chapter 3 Materials_&Methods

This section provides detailed description of the sample used in this study, selection criteria of teeth, methods used in the analysis for age estimation. Using root dentine translucency and dental root color. It will represent the protocols for preparing the sample for each phase of analysis, equipment, and methods for data collection, and statistical analysis adopted.

3.1 Dental charting method

There have been several notational methods for dental charting, all of which have been devised as a shorthand to quickly identify a tooth without writing the entire cumbersome anatomic description (Sopher 1976, Hillson 1996). Today, there are over thirty different systems for charting teeth (Clark 1991). In 1971, the Fédération Dentaire Internationale (FDI) devised a system which is used throughout the world by several organizations, such as Interpol, World Health Organization, and the International Association of Dental Research. The FDI system provides a unique two-digit number for each tooth. The first number in the pair represents the quadrants and the second number delineates the tooth, numbered from mesial to distal. Any number beginning with 1 represents the permanent maxillary right quadrant, 2 represents permanent maxillary left, 3 permanent mandibular left, and 4 permanent mandibular right. Deciduous quadrants are delineated with the first numbers 5-8 in the same fashion. This system allows for quick entry into a computer database with a unique number representing each tooth and was utilized for the following research (Fig 3.1).

Fig 3.1 Different dental charting methods for permanent teeth

Abbildung in dieser Leseprobe nicht enthalten

3.2 Sample derivation and characteristics

3.2.1 Collection Site

Samples were collected mainly from elven (11) sites in the Nubian resettlement area (new Nubia) in Kom Ombou district south of Egypt namely the villages of Al-Maliki, Wadi el Arab - Al Subu (Arabs villages) and Tuski-west, Adindan, Al Diwan and Ibrim (Nubians villages), Al Ambirkab, Dahmit, Kalabsha, Abu hur (Kenuz villages)

Fig 3.2 A map of New Nubia showing the sites of sample collection.

Abbildung in dieser Leseprobe nicht enthalten

3.2.2 Sample characteristics

The sample consisted of 416 freshly extracted permanent teeth collected from 311 adult patients referred to several dentists and dental clinics both in the private clinics and dental units of ministry of health and population in Kom Ombou district, south Egypt in the Nubian resettlement area. In two and half years’ time single rooted teeth maxillary and mandibular incisors, canines, and premolars were collected and included in the study. In all cases, teeth were extracted from Nubian people belonging to one of four main Nubian groups (Kenouz, Arabs, Nubians (Fadija), and Halfans). Tooth extractions were
performed as part of essential clinical care as for periodontal, periapical, orthodontic and prosthesis construction reasons . In addition to the extraction date of the tooth and reason for extraction, the records contain the chronological age of the donor at extraction, sex, and ethnicity, occupation.

Among the 416 teeth in the sample, 160 originated from multiple extractions of 55 individuals. In most cases, 2 teeth per individual were available, although as many as 6 teeth were extracted from a single individual. The sample distribution according to tooth type, sex, age groups and ethnic background are shown in tables (3.1, 3.2) and illustrated in graphs (3.1, 3.2)

Table 3.1 Sample distribution according to tooth type and sex.

Abbildung in dieser Leseprobe nicht enthalten

Graph 3.1 Sample distribution according to tooth type and sex.

Abbildung in dieser Leseprobe nicht enthalten

Table 3.2 Sample distribution according to chronological age and sex.

Abbildung in dieser Leseprobe nicht enthalten

Graph 3.2 Sample distribution according to chronological age and sex.

Abbildung in dieser Leseprobe nicht enthalten

3.3 Protocol for sample collection

3.3.1 Sample selection

The study group utilized in the present study consisted of 416 objects, with age ranging between 20 to 85 years. The inclusion criteria for the study were as follows:

- Adult male and females of Nubian origin
- Single rooted maxillary and mandibular teeth
- Extracted tooth with complete root (i.e. if fractured the tooth is excluded from the sample):
- Teeth should be unaffected by caries, periodontitis, abscess, or other pathological processes causing exposure of the root to the oral environment.
- In all the cases the dental extraction procedure was done as part of essential dental care.

3.3.2 Documentation

- In addition to the extraction date of the tooth and reason for extraction, the records contain the chronological age of the donor at extraction, sex, and ethnicity, occupation.

- A sample collection chart was designed for the documentation of the sample used in the present study ( Fig 3.4).

3.3.3. Extraction

- Dental extraction was done using dental forceps specially assigned for the tooth type (i.e. Maxillary anterior forceps, mandibular premolar forceps etc).
- Extraction was done with minimal trauma to both tooth and surrounding bone.

3.3.4.Disinfection

After extraction, each tooth was rinsed with water and the tissue remains were removed with tweezers and scalpel from the root. The tooth was then disinfected with 5% Na-hypochlorite for 10 minutes and the root surface of the tooth was smoothen with a small rubber disc to remove all the reaming tissues attached to the root .

3.3.5.Storage

Every tooth was then closed into small plastic tubes filled with distillated water. Distillated water is used because it will not alter the mineral concentration of the tooth surfaces, hence will not interfere with both root dentine translucency and dental root color analysis (Fig 3.3).

Fig 3.3 Teeth stored in small plastic tubes filled with distillated water

Abbildung in dieser Leseprobe nicht enthalten

Fig 3.4 Sample collection chart and Data coding scheme used in the present study

Abbildung in dieser Leseprobe nicht enthalten

Sample collection chart

A. Personal data

Name: Date of Donation:

Gender: Ethnicity:

Date of birth:

Birth place: Marital status:

Socioeconomic Status: Occupation:

B. Reason for extraction:

1) Dental caries, 2) Periodontal disease, 3) Orthodontic care prosthetics, 5) Multiple pathologies

C. Medical history:

_Have you had / Do you have (l.Yes 2.No) (Duration)

1. Heart disease
2. High blood pressure
3. Liver trouble
4. Kidney trouble
5. Are you Diabetic?

a. Insulin dependent diabetes b. Non Insulin dependent c. Amount of Insulin (Unit) d. Oral hypoglycemic drugs

Blood glucose Level

Dental analysis:

Tooth Type: Tooth Number:

1. Root dentine translucency:

Root height (RH) using caliper: Root height using image analysis:

Root dentine translucency (RDT) using caliper:

Root dentine translucency (RDT) using Image analysis:

RDT/RH[1] 100=

2. Color analysis

R= G= B= RGB= RGB INDEX=

Abbildung in dieser Leseprobe nicht enthalten

Data coding scheme for sample collected in the present study.

Abbildung in dieser Leseprobe nicht enthalten

Site Identification (1,2): Each site of data collection is identified using a 2 digit alpha/numeric code e.g. A1.

- Individual Identification (3,4,5) : Each individual in the sample is assigned a unique three digit identification number e.g. 034.
- Sex(6) : each individual sex is assigned a one digit number (0 for males and 1 for females )
- chronological age of the donor at extraction (7,8,9,10) : is recorded as a four (4) digit numeric field with one decimal place e.g. 24.5 years
- Date of birth (11,12,13,14): is recorder as four (4) numeric characters. The first two denotes the month and the later denotes the year e.g. 0478 this means April the year 1978
- Date of Donation of the tooth (15,16,17,18) : is recorder as four (4) numeric characters. The first two denotes the month and the later denotes the year
- Ancestry (19): it is a one digit alpha/numeric code denoting the ancestry of the individual to one of the 4 major Nubians groups (1:Kenouz 2: Arabs 3:Nubians (Fadija) 4: Halfans).

3.4 Protocol for sample preparation for root dentine translucency and dental root color analysis

3.4.1 Root dentine translucency measurement

Quantifications of apical translucency have been suggested in several different formats: subject indices (Gustafson 1947, 1950, 1955, Johanson 1971), direct measurement Írom the apex towards the CEJ (Miles 1963, Bang and Ramm 1970,Sengupta et al. 1998, 1999), area of translucency (Lorentsen and Solheim 1989, Sengupta et al. 1998, 1999), length expressed as a proportion of the total root (Lamendin and Cambray 1980, Drusini et al. 1991, Lamendin et al. 1992, Thomas et al. 1994, Sengupta et al. 1998, 1999, Prince and Ubelaker 2002, Sarajliæ et al. 2003), area expressed as a proportion of the total root area (Johnson 1968, Vasiliadis et al. 1983, Drusini et al. 1991,Sengupta et al. 1998, 1999), computer-assisted image analysis (López-Nicolás et al.1990, 1993, 1996, Drusini et al. 1991, Sengupta et al. 1998, 1999), and by total volume (Rathod et al. 1993, Manly and Hodge 1939).

Root dentine translucency measurements were assessed in the present research using two methods for measuring both the direct length from the apex towards the CEJ and length expressed as a proportion of the total root namely: The traditional method (manual) and The image analysis method.

3.4.1.1 Manual measurement of root dentine translucency RDT (the traditional method)

Measurements were taken according to the method outlined in Lamendin et al., (1992). This includes the assessment of the root height using a pair of sliding calipers, a millimeter ruler, and a desktop lamp. The root height is defined according to Lamendin et al., as ‘‘the distance between the apex of the root and the cementoenamel junction, measured on the labial surface of the tooth’’(1992:1375). Root dentine translucency was measured on the labial surface of the tooth and is described as “the length of the transparent zone extending from the junction between the translucent and opaque areas of the root to the tip of the root”. (1992:1375) Due to the different patterns of root dentine translucency observed in our sample a new measurement system depending upon the pattern of root morphology and translucent zone extent was used and is illustrated in fig 3.6

Fig 3.5 Instruments used in manual measurement of RDT (2 dividers - Sliding digital caliper- Ruler - Compass divider).

Abbildung in dieser Leseprobe nicht enthalten

Fig 3.6 1 Different measuring technique of RDT according to the morphological pattern of RDT ( three measurments are taken at the mesial, distal ,and middle portionsof the labial surface of the root and RDT is expressed as the mean values of the three measurements taken).

Abbildung in dieser Leseprobe nicht enthalten

3.4.1.2 Image analysis measurements of root dentine translucency

Each tooth was digitally recorded using a digital camera (Nikon D100, with illumination of Multiblitz 400 image analysis software (Digimizer 3.6®). Ruler tool in Adobe Photoshop CS3®) was used to measure the root translucency length and the root height. Image analysis was performed to enhance the determination of the exact length of the root dentine translucency by using image filters in Adobe Photoshop CS3® extended software (Fig 3.7). Both root dentine translucency RDT and root height RH are measured. The computer- assisted measurements (CAM) were recorded by the observer defining reference points on the images of the tooth as viewed on the screen (Fig 3.8).

Fig 3.7 Application of image enhancement techniques for Computer assisted measurement of root dentine translucency, first image is a digital image of the toot without image enhancement, other images show same image after applying digital filters to enhance RDT measurement.

Abbildung in dieser Leseprobe nicht enthalten

Fig 3.8 Digital measurement of RDT using Digimizer 3.6®

Abbildung in dieser Leseprobe nicht enthalten

3.4.2 Color analysis

In 1869, James Clerk Maxwell asked photographer Thomas Sutton (the inventor of the SLR camera) to take three black-and-white photographs of a tartan ribbon. Maxwell wanted to test a theory he had about a possible method for creating color photographs. He asked Sutton to place a different filter over the camera for each shot: first, a red filter, then green, and then blue. After the film was developed, Maxwell projected all three black-and-white pictures onto a screen using three projectors fitted with the same filters that were used to shoot the photos. When the images were projected directly on top of each other, the images combined and Maxell had the world’s first color photo. This process was hardly convenient. Unfortunately, it took another 30 years to turn Maxwell’s discovery into a commercially viable product. (Itten 1990, Throuex 1994, 1996)

This happened in 1903, when the Lumière brothers used red, green, and blue dyes to color grains of starch that could be applied to glass plates to create color images. They called their process Autochrome, and it was the first successful color printing process. Painters have used this technique for centuries, of course, but what Maxwell demonstrated is that, although you can mix paints together to create darker colors, light mixes together to create lighter colors. Or, to use some jargon, paint mixes in a subtractive process (as you mix, you subtract color to create black), whereas light mixes in an additive process (as you mix, you add color to create white) (Fig 3.9). Note that Maxwell did not discover light’s additive properties—Newton had done similar experiments long before—but Maxwell was the first to apply the properties to photography

Fig 3.9 Additive and subtractive color spaces, Additive mixing blue, green and red light and subtractive color synthesis using yellow, magenta and cyan colorants (Poynton, 2003)

Abbildung in dieser Leseprobe nicht enthalten

3.4.2.1 Color description

There are many different ways to describe color, the (RGB) color model is an additive model used for displaying images on a computer monitor or other screen device: When the three primary colors, red, green, and blue, are combined they make white light: When the three colors are absent, the color is black: The RGB color model is the most common way to encode color in computing, and several different binary digital representations in use:

The main characteristic of all of these representations is the use of the quantification values per component by using only integer numbers within some range usually form 0 to a some power of two minus one (2n - 1) to fit them into some bit groupings. The RGB values encoded in 24 bits per pixel (bpp) are specified using three 8-bit unsigned integers (0 through 255) representing the intensities of red, green, and blue (Fig 3.10): This representation is the current mainstream standard representation for the so-called the true color. It allows more than 16 million different combinations many of them indistinguishable to the human eye. (Poynton, 2003)

Fig 3.10 The RGB color system and the difference between analogue and digital recording of color (Poynton, 2003)

Abbildung in dieser Leseprobe nicht enthalten

3.4.2.2 RGB Color analysis

A major criticism to the studies used the traditional methods of color description is related to the subjective manner in which the tooth color is assessed. This methodology ranks the color on a visual scale by mean of a descriptive standard. The visual scales have the flaw of lacking reproducibility in the assessment. To remedy this problem computer assisted analysis will be used for the tooth image. Consequently this technique establishes a methodology that is reproducible, objective and accurate.

The quantitive analysis of the tooth root color was made using Red Green Blue system (RGB). In order to show possible differences in color on the root surface and to choose safer determination of the average color value, two measurements were made. In the first measurement the RGB values were analyzed on the surface that covers all three root areas (cervical, middle and
apical third). In the second measurement the RGB values were analyzed only on the middle root third. For standardization of the technique a color specimen is taken in the middle one third of the root by using a grid applied to the buccal or the labial surface of the root (2mmx2mm). (Fig 3.11)

Fig 3.HColor analysis of the root by applying a grid to the labial surface of the tooth and taking a color specimen of 2mmX2mm from the middle part of the root and analyzing the color expressing the value of color as the mean value of the red ,blue and green components (RGB).

Abbildung in dieser Leseprobe nicht enthalten

3.5 Protocol for Statistical analysis for root dentine translucency and dental root color analysis

Data were exported to an Excel spread sheet and analyses were performed using the SPSS statistical package (version 17.0) for PCs. A test of normality for the sample distribution was performed in order to utilize the appropriate statistical methods depending on whether the data are parametric or non- parametric in nature. Sperman’s rho and Kendall’s tau correlation procedure was adopted to find correlations between chronological age (CA) and the length of the root dentine translucency as well as dental root color. Results obtained from the two techniques - traditional (RDTM) and computer assisted image analysis (RDTC) used to assess length of the root dentine translucency were tested for correlation with CA. Moreover, an index for dentine translucency (RDTI) was calculated according the following formula

RDTI= RDT/RH *100

Where RH is the root height

Regression equations were driven for the relation between each of the above mentioned parameters (RDT and RDTI) and the chronological age (CA).

Similarly the relation between each component of dental root color (red, green, blue) and the chronological age were investigated. On the other hand, dental root color as expressed by RGB values and the calculated color index RGBI were also investigated and a regression equations was driven for correlation with (CA). The dental color index was calculated using the following formula

RGBI=RGB/RGBW*100

Where RGBW is the correspondent RGB value of white in the color system used for the
image analysis (i.e. using the 8-Bit color system as in the study RGBW=255)

Regression equations were driven to express the relationship between the chronological age and RGB and RGBI individually and in conjunction using multiple regression analysis.

In order to assess the validity of the derived equations in this study different validation procedures were performed

1) Fortunately some authors published the raw data obtained from their samples. Using the published data we can test the accuracy of the derived equations by using the parameters in the published data in our regression equations and compare the estimated age to the true published chronological age.
2) Using resampling technique procedures, in statistics, resampling is any of a variety of methods for estimating the precision of sample statistics (medians, variances, percentiles) by using subsets of available data (jackknifing) or drawing randomly with replacement from a set of data points (bootstrapping) and Validating models by using random subsets.

3) By using cross validation, Cross-validation, sometimes called rotation estimation, is the statistical practice of partitioning a sample of data into subsets such that the analysis is initially performed on a single subset, while the other subset(s) are retained for subsequent use in confirming and validating the initial analysis. The initial subset of data is called the training set; the other subset(s) are called validation or testing sets. In this study holdout validation is used. Observations are chosen randomly from the initial sample to form the validation data, and the remaining observations are retained as the training data. Normally, less than a third of the initial sample is used for validation data.

In order to explore the impact of gender on RDT and RGB a unit index was calculated for the rate of development of apical root dentine translucency and change in dental root color using the following formulas:

RDTF = ((RDT/RH) / CA) *100

Where RDTF refers to the rate of formation of RDT per unit year , RDT root dentine translucency , RH root height , CA chronological age

RGBF = ((RGB/ RGBW)/CA) *100

Where RGBF refers to rate of change in dental root color per unit year, RGB dental root color, RGBW the value of white in the color system used (i.e. 255 in the 8 bit color system) , CA chronological age

Figure 3.12 illustrates the Protocol for Statistical analysis for root dentine translucency and dental root color analysis used in the current research

Abbildung in dieser Leseprobe nicht enthalten

Fig 3.12 Statistical analysis in the current research

Chapter 4 Results.

The sample used in the present study consisted of 416 single rooted maxillary and mandibular teeth with age ranging from 15.7 to 82.1 years and a mean of 52.138 years ± 12.31years. To test the normality of the sample two tests were performed Shapiro-Wilk W test and Anderson-Darling test[1]. The results of the two tests were (0.978 p value 1.622X10- 5) and (2.838 with p value 3.890X10-5) respectively revealing that the data is not normally distributed (Graph 4.1). Thus all the statistics applied to the data will be non-parametric tests as non-parametric statistics uses distribution free methods which do not rely on assumptions that the data are drawn from a given probability distribution (i.e. normal distribution).

Graph 4.1 Histogram of the age distribution of the sample, with a QQ (Quantile-quantile[2] ) plot to assess the normality of the sample distribution

Abbildung in dieser Leseprobe nicht enthalten

4.1 Root dentine translucency

Results of correlation between chronological age (CA) and parameters measured for RDT are shown in table 5.1. The correlation between manual measurement of root Dentine translucency (RDTM) and CA shows a strong linear relation the calculated spearman rho[3] and Kendall tau[4] correlation coefficients of 0.954 and 0.840 respectively. Similarly, the correlation between CA and computer assisted measurement of root dentine translucency (RDTC) has produced strong linear correlation with a spearman rho and Kendall tau correlation coefficients of 0.957 and 0.849 respectively (Graph 4.2) .

Correlation between manual measurements of root dentine translucency (RDTM) and computer assisted measurement of root dentine translucency (RDTC) (Graph 4.3) shows very strong linear correlation with and a spearman rho and Kendall tau correlation coefficients of 0.997 and 0.971 respectively. On the other hand, correlation between root dentine translucency index (RDTI) and (CA) show a strong linear relation with a spearman rho and Kendall tau correlation coefficients of 0.952 and 0.823respectively. (Table 4.1)

Abbildung in dieser Leseprobe nicht enthalten

Table 4.1 spearman rho and Kendall tau correlation coefficients matrix between chronological age (CA) and root dentine translucency manually measured (RDTM) and computer assisted measurements (RDTC) and root dentine translucency index (RDTI).

**. Correlation is significant at the 0.01 level (2-tailed).

Graph 4.2 A 3D border display chart showing the relation between CA and RDTM, RDTC against each other with the distribution of the two variables appearing at the border in this graph.

Graph 4.3 A 3D border display chart showing the relation between CA and RDTI left graph and RDTM, RDTC right graph.

Abbildung in dieser Leseprobe nicht enthalten

4.2 Color analysis

Results of correlation between chronological age and parameters resulted from the dental root color analysis is shown in table 4.2 and graph 4.4. The correlation between chronological age and red component of the color (R) shows a strong linear relation. The spearman rho and Kendall tau correlation coefficients were -0.979 and -0.935 respectively.

Table 4.2 spearman rho and Kendall tau correlation coefficients matrix between (CA) and RGB values where R refers to the red component of color G refers to the green component of color and B to the blue component of color, RGB mean value of color is the mean of the R, G,B values and is considered to be an integral value of the color.

Abbildung in dieser Leseprobe nicht enthalten

**. Correlation is significant at the 0.01 level (2-tailed).

Meanwhile, the correlation between chronological age and green component of color (G) revealed a strong linear relation with Spearman rho and Kendall tau correlation coefficients of -0.979 and -0.928 respectively. Correlation between chronological age and blue component of color (B) has also a strong linear relation. The statistical analysis of data shows Spearman rho and Kendall tau correlation coefficients of -0.981 and -0.940 respectively (Table 4.2).

Graph 4.4 A 3D border display chart showing the relation between CA and R, G, B (upper 3 graphs) and CA and RGBI, RGB (lower 2 graphs).

Abbildung in dieser Leseprobe nicht enthalten

Abbildung in dieser Leseprobe nicht enthalten

Correlation between chronological age CA and RGB of color shows again a strong linear relation with Spearman rho and Kendall tau correlation coefficients of -0.989 and -0.944 respectively. Similarly, correlation between chronological age and RGBI of color shows a strong linear relation with Spearman rho and Kendall tau correlation coefficients of -0.989 and -0.944 respectively (Graph 4.4).

4.3 Regression analysis

Multiple regression analysis procedures were performed with age as dependent variable (Graph 4.5). The resulting formulae of age estimation were then established as follows:

Age = 23.542+ 6.157 RDT

r2 = 0.903; absolute mean error of estimation 5.607 years, where RDT refers to the root dentine translucency measured using image analysis.

Age= 22.599+0.84486 RDTI

r2 = 0.95; absolute mean error of estimation 3.16 years, where RDTI refers to the root dentine translucency index

Age=160.22-0.67066 RGB

r2 = 0.97; absolute mean error of estimation 3.76 years, where RGB refers to the root color measured using image analysis and expressed in RGB color Space.

Age= 160.26-1.711RGBI

r2 = 0.97; absolute mean error of estimation 2.76 years, where RGBI refers to the root color index measured using image analysis

Age= 128.832-1.329RGBI+ 0.2084RDTI

r2 = 0.97; absolute mean error of estimation 2.996 years, where RGBI refers to the root color index, RDTI refers to the root dentine translucency index

Age=130.0729-0.526RGB+ 1.467RDT

r2 = 0.97; absolute mean error of estimation 3.65 years. Where RDT refers to the root dentine translucency measured using image analysis. RGB refers to the mean value of dental root color

Graph 4.5 A: residuals of the regression[5] between CA and RDT, RDTI , B: residuals of the regression between CA and RGB,RGBI C: a sequentional plot of the relation between CA and RDTM, RDTC,RDTI (right) and CA and RGB,RGBI (left).

Abbildung in dieser Leseprobe nicht enthalten

4.4 Validation ofthe regression equations

4.4.1 Application to published data

In order to test the applicability of the regression equations derived from the present study we applied them to published data by Saglam Atsu et al., (2006)[6] In their research 21 single root teeth were examined, which were extracted from individuals with known age that ranges from 13 to 70 with a mean of 47.57 and standard deviation of 13.614. The authors concluded that Age estimation from teeth can be performed from the ratio of length of root transparency to total root length (r2= 0.88), with ± 4.9 years error.

Table 4.3 correlation coefficients between published age CA and age estimates using RDT and RDTI equations driven in this study

Abbildung in dieser Leseprobe nicht enthalten

The equations derived from the present study were applied to Saglam’s published data to estimate age using RDT and RDTI. The correlation coefficient between the published chronological age (CA) and results of age estimates from RDT and RDTI equations of the present study (AERDT, AERDTI respectively) was a strong linear correlation (Table 4.3- Graph 4.6). The calculated spearman rho and Kendall tau correlation coefficients for AERDT and chronological age were 0.855 and 0.726 respectively. Meanwhile the calculated spearman rho and Kendall tau correlation coefficients for AERDTI chronological age were 0.855 and 0.722 respectively with a Cronbach’s alpha[7] coefficient between the three variables (CA, AERDT, and AERDTI) of 0. 944

Graph 4.6 A: sequentional graph between CA and AERDT, AERDTI B: A box plot showing the median and the distribution of CA , AERDT, AERDT where CA refers to published data.

Abbildung in dieser Leseprobe nicht enthalten

4.4.2 Validation ofthe results using resamplingtechniques

Both jackknifing and bootstrapping were applied to the sample to check the validity of the predictive models and correlation stated in our results (graph 5.7, 5.8). Jackknife estimate of spearman correlation were 0.958, 0.953,-0.989, -0.989 while bootstrap estimate of spearman correlation were 0.956, 0.956, -0.989,-0.989 for RDT, RDTI, RGB, RGBI respectively. The graphs suggest that correlation between the different parameters used in this study was not affected by resampling indicating consistency of the results.

Abbildung in dieser Leseprobe nicht enthalten

Graph 4.7 Jackknifed spearman correlations between CA and RDT, RDTI, RGBI

Abbildung in dieser Leseprobe nicht enthalten[8]

Graph 4.8 Bootstrapped spearman correlations between CA and RDT, RDTI,RGBI

4.4.3 Hold out cross validation

The validating sample composed of 132 cases (32% of the total sample) randomly selected as validating sample (Graph 4.8) with age ranging from 15.7­ 74.3 a mean of 47.57 years and a standard deviation of 13.614 years. The regression equations derived from the training sample were used to calculate age using root dentine translucency (AERDT), root dentine translucency index (AERDTI), mean value of color (AERGB), index of mean value of color (AERGBI) and the multiple regression equations using mean value of color and root dentine translucency (AERGB-RDT) and indices of root dentine translucency and mean value of color (AERGBI-RDTI). The obtained spearman rho correlation coefficients were 0.936, 0.901, 0.898, 0.898, 0.922, 0.919 respectively, and Ken dall’s tau Correlation coefficients of 0.816, 0.765, 0.801, 0.801, 0.798 respectively (Table 4.4). The correlations showed the linear relations between chronological age and estimated age from different parameters (Graph 4.10, 4.11) .The Cronbach’s alpha of the variables is 0.988.

Graph 4.9 The percent of the validating and the training samples

Abbildung in dieser Leseprobe nicht enthalten

Table 4.4 spearman rho and Kendall tau correlation coefficients between CA and age estimates from different regression equations.

Abbildung in dieser Leseprobe nicht enthalten

**. Correlation is significant at the 0.01 level (2-tailed).

Graph 4.11 sequentional graph between CA age estimates from different regression equations.

Abbildung in dieser Leseprobe nicht enthalten

4.5.The effect of sex on RDT and RGB.

Results of correlation between the male root dentine translucency formation rate (MRDTF) and female root dentine translucency formation rate (FRDTF) shows a significant linear correlation with a spearman rho and Kendall tau correlation coefficients of 0.581 and 0.408 respectively. Similarly correlation between the male dental root color change rate (MRGBF) and female dental root color change rate (FRGBF) shows a significant strong linear correlation with a spearman rho and Kendall tau correlation coefficients of 0.993 and 0.952 respectively (Table 4.5 , Graph 4.12). Graphs 4.13 and 4.14 shows histograms for different rates of root dentine translucency formation and dental root color change between males and females.

Graph 4.12 A 3D border display chart showing the relation between MRDTF, FRDTF and MRGBF, FRGBF

Abbildung in dieser Leseprobe nicht enthalten

Graph 4.13 Histograms of the different rates of root dentine translucency formation between males and females.

Abbildung in dieser Leseprobe nicht enthalten

Table 4.5 Spearman rho and Kendall tau correlation coefficients between MRDTF, FRDTF, MRGBF, FRGBF.

Abbildung in dieser Leseprobe nicht enthalten

**. Correlation is significant at the 0.01 level (2-tailed).

Graph 4.14 Histograms of the different rates of dental root color change between males and females.

Abbildung in dieser Leseprobe nicht enthalten

Mann-Whitney rank sum test[9] was performed to detect the presence of significant difference between different formation rates for root dentine translucency and dental root color change among both males and females. Results of the Mann-Whitney rank sum test between the FRDTF and MRDTF had a Mann-Whitney U Statistic= 19552.000, T Value =30283.000 and P value = 0.893 thus The difference in the median values between the two groups is not great enough to exclude the possibility that the difference is due to random sampling variability; there is no statistical significant difference (P = 0.893). Similarly results of the Mann-Whitney rank sum test between the FRGBF and MRGBF had a Mann-Whitney U Statistic= 18470.000, T Value = 31681.000 and P value = 0.290 thus The difference in the median values between the two groups is not great enough to exclude the possibility that the difference is due to random sampling variability; there is no statistical significant difference (P = 0.290).

Chapter 5 Discussion and Conclusions

The human skeleton is an exceedingly complex and dynamic system which continuously responds and reacts to internal and external stimuli (Currey, 1964; Mckem, 1970; Frost, 1980; Angel, 1986; Igbigbi, 2005; Bosmans, 2005; Meinl et al., 2007; Olze et al., 2007; Schulz et al., 2008 a,b) one critical variable that inevitably leaves its imprints on different parts of the skeleton is the aging process. There are well documented landmarks associated with this aging process. Generally speaking, methods of age determination of skeletal remains are based mainly on one or more of the following: the amount of growth; the stage of development; and the amount of degenerative changes (.Currey, 1964; Angel, 1986; Mckern, 1970; Kerely, 1970; Stewart, 1979; Schmidt et al., 2008 ; Cameriere et al., 2008 )

Age assessment Írom the skeletal remains presents some problems. Firstly, the accuracy of age determination depends largely on-the nature of the materials: i.e., the state of preservation, degree of completeness of the individual skeleton, and the particular age group to which the skeleton belongs. Secondly, it is well known that methods of aging the adult skeleton are not equally reliable, and accuracy in age assessment drops considerably with advancing age. Thirdly, the skeletal and/or dental ages are assumed to closely approximate chronological age. The difference between them is an "unknowable" factor (Shaaban, 1988). Almost all established macroscopic methods for age estimation in the skeleton are problematic (Buikstra, 1994; Wood et al., 2002; Solheim & Vonen, 2006). This is because only changes in biological age can be observed in skeletons. High interindividual variability results in error margins that may reach 5 years, at best, for ages after skeletal growth is complete (Prince, 2004). The problem intensifies at older ages, as individual variability of age-dependent changes in the skeleton increases. Thus, methodological problems increase with the age of the person.

Teeth are important aging elements because they have a vast postmortem longevity due to their highly mineralized composition. As such, they are the most durable structure in the human body, more resilient than bone, and highly resistant to physical and chemical influences. Teeth are frequently the only remains recovered from forensic scenes and archaeological sites (Maples, 1978; Marcsik et al.,1992; Ohtani, 1995; Schmeling et al., 2007 & 2008; Landa et al., 2009). Physical anthropologists utilize dental features in three main areas of research namely: 1) paleontology, where teeth form the basis for many reconstructions of phylogenetic relationships; 2) skeletal biology, where teeth are considered as medium by which individuals survive in their environment and a tool to study populational adaptations to environmental factors; 3) forensic anthropology, where teeth are used for identification purposes. ( Reppien et al., 2006)

Several researchers have developed techniques to determine age-at-death for adults by employing the dentition tissues and dental morphology. Most methods involve assessing age-related changes in attrition (Zuhrt, 1955; Miles, 1962, 1963; Brothwell, 1963; Lavelle, 1970; Molnar, 1971; Ito 1972,1975; Lunt, 1978; Miles, 1978; Scott, 1979; Smith, 1984 a, b; Lovejoy, 1985; Brothwell, 1989; Li and Ji, 1995), secondary dentin deposits (Morse et al., 1993; Kvaal and Solheim, 1994), cementum apposition (Charles et al., 1986; Condon et al., 1986; Wittwer-Backofen, 2000; Wittwer-Backofen and Buba, 2002; Wittwer-Backofen et al., 2004), apical translucency (Bang and Ramm, 1970), periodontal recession (Solheim, 1992; Borrman et al., 1995), root resorption (Borrman et al., 1995), acid racemization (Helfman and Bada, 1975; Helfman and Bada, 1976; Shimoyama and Harada, 1984; Ogino et al., 1985; Masters, 1986; Ritz et al., 1990; Ohtani and Yamamoto, 1991, 1992; Ritz et al., 1993; Mörnstad et al., 1994; Ohtani, 1994, 1995; Ohtani et al., 1995; Carolan et al., 1997), color change of the root (Ten Cate et al., 1977; Solheim, 1988; Borrmann et al., 1995), or a combination of several of these indicators (Gustafson, 1947, 1950, 1955; Johanson, 1971; Maples, 1978; Maples and Rice, 1979; Matsikidia and Schultz, 1982; Kashyap and Koteswara Rao, 1990; Lamendin and Cambray , 1980; Lamendin et al., 1992; Solheim, 1993; Kvaal et al., 1995; Russell, 1996). Several researchers have analyzed these features individually and in conjunction.

There have been several notational methods for dental charting, all of which have been devised as a shorthand to quickly identify a tooth without writing the entire cumbersome anatomic description (Sopher 1976, Hillson 1996). The FDI system used in the present study provides a unique two-digit number for each tooth. Which is used throughout the world by several organizations, such as Interpol, World Health Organization, and the International Association of Dental Research. The utilization of this system is in accordance with Foti et al., 2001; Prince, 2004; Brkic et al., 2006.

The selected samples were single-rooted teeth to exclude anatomical variations and complexity factors associated with measurement procedure for both root dentine translucency measurement and dental root color analysis. Tooth specimens included were fully erupted permanent teeth extracted for valid clinical reasons (periodontal disease, malocclusion/orthodontic treatment, and caries). Carious teeth were included in the sample contingent to roots being unaffected macroscopically by disease. This was in line with Foti et al., 2001; Olze et al., 2004; Acharya et al., 2009 .

One purpose of this study was to examine the relationship between root dentine translucency and chronological age. Several studies have been done to test such a relation in different population but none have been done for the Egyptian Nubians. In the present study two different methods were adopted to quantify root dentine translucency extent using the traditional (manual) technique and computer assisted image analysis technique. Several authors have reported that translucency of the root is the best dental indicator of age and most closely correlated to chronological age (Gustafson, 1950; Miles, 1963; Bang and Ramm, 1970; Johanson, 1971; Maples, 1978; Metzger, 1980; Solheim and Sundnes, 1980; Kósa et al., 1983; Vasiliadis et al., 1983; Sognnaes et al., 1985; Lorensten & Solheim, 1989; Solheim, 1989; López-Nicolás et al., 1990, 1993& 1996; Sengupta et al.,1998& 1999; Ajmal et al., 2001). However, translucency apposition may be influenced by genetic, environmental, and cultural factors (López-Nicolás et al., 1996). Nonetheless several researchers (Vlèek and Mrklas, 1975; Marcsik et al., 1992; Sengupta et al., 1999) have stated that apical translucency was not a reliable age indicator.

Quantifications of apical translucency have been suggested in several different formats: subject indices (Gustafson, 1947, 1950 & 1955, Johanson, 1971), direct measurement from the apex towards the CEJ (Miles, 1963; Bang & Ramm, 1970;Sengupta et al., 1998& 1999), area of translucency (Lorentsen & Solheim, 1989; Sengupta et al., 1998& 1999), length expressed as a proportion of the total root (Lamendin & Cambray, 1980; Drusini et al., 1991; Lamendin et al., 1992; Thomas et al., 1994; Sengupta et al., 1998 & 1999, Prince & Ubelaker, 2002; Sarajliæ et al., 2003), area expressed as a proportion of the total root area (Johnson, 1968; Vasiliadis et al., 1983; Drusini et al., 1991; Sengupta et al., 1998& 1999), computer-assisted image analysis (López-Nicolás et al., 1990; 1993& 1996, Drusini et al., 1991; Sengupta et al., 1998& 1999), and by total volume (Manly & Hodge, 1939; Rathod et al., 1993). In the present study both the use of direct measurement and the length expressed as a portion of the total root were used to quantify root dentine translucency.

Translucency of the root can been analyzed in longitudinal thin sections (Gustafson, 1947, 1950, 1955; Dechaume et al., 1960; Nalbandian et al., 1960; Johanson, 1971; Solheim and Sundnes, 1980; Vasiliadis et al., 1983; Whittaker and Bakri, 1996; Sengupta et al., 1998, 1999) or on intact teeth (Bang and Ramm, 1970; Colonna et al.,1984; Solheim, 1989; Drusini et al., 1991; Lamendin et al., 1992; Prince and Ubelaker, 2002; Sarajliæ et al., 2003). Translucency of the root can be seen macroscopically, but is enhanced with the aid of a light box. There are several advantages of taking measurements directly from intact teeth: it is non-destructive, less expensive and less time consuming than other methods and it is not necessary to have a complete knowledge of dental histology.

However, there are some technicial difficulties related to the manual measurement of root dentine translucency which can limit its usefulness: 1.) within the constraints of the root morphology, the teeth must be positioned in such a way that the translucency is visible from apex to CEJ and 2.) the most useful position of the tooth has yet to be standardized.

In the present research intact teeth are used rather than using of sectioned teeth as most researchers have reported that it is difficult to make sections that are representative of the real level of translucency in the root. The translucency may develop in a butterfly shaped pattern in the transverse plane, which would be misrepresented by longitudinal sections (Darling & Levers, 1983). This difficulty is also partially due to twisted and bent roots that can complicate sectioning through the ‘center’ of the root. Thus there has been a wide range of variation in the accuracy and precision of age estimations made using root dentine translucency using sectioned teeth.

Results of both techniques have shown to be in strong linear correlation to the chronological age (r = 0.95 and 0.957 respectively) and in strong correlation with one another(r = 0.997). This means that image analysis can be used as a comprehensive technique to measure RDT rather than using manual techniques. Computer assisted image analysis provides many advantages over traditional (manual) measurement for RDT. Digital techniques produced a dynamic, rather than static, image in which visual characteristic of density and contrast could be manipulated after acquisition to meet specific diagnostic tasks or to correct errors in exposure technique. The use of digital technology also resulted in a more accurate and reproducible assessment for RDT and thus ensure a more consistent procedure of measuring RDT.

The strong correlation between RDT and CA obtained in this study are in agreement with previously published results (Gustafson, 1950; Miles, 1963;Bang and Ramm, 1970; Johanson, 1971; Maples, 1978; Metzger, 1980;Solheim and Sundnes, 1980; Kósa et al., 1983; Vasiliadis et al., 1983; Sognnaes et al., 1985; Lorensten and Solheim, 1989; Solheim, 1989; Kashyap & Koteswara, 1990; López-Nicolás et al., 1990, 1993 & 1996, Drusini et al., 1991; Sengupta et al., 1998&1999; Ajmal et al., 2001; Olze et al., 2004; Ubelaker, 2008; Acharya, 2009 ). The formulated regression equations based on these two variables can be used to estimate age at death efficiently with a standard mean error ranging from ± 3.16 to ± 5.067 for RDTI and RDT respectively.

This correlation may be explained in the light of the ultrastructure nature of root dentine translucency. Root dentine becomes increasingly translucent with age, the process commencing at the root apex and progressing towards the CEJ (Nalbandian and Sognnaes, 1960). Dentine normally appears opaque because of different refractive indices of the crystalline ‘fundamental matter’ and the intratubular organic matrix (Drusini, 1989). With advancing age, the continued deposition of intratubular dentine can lead to the complete obstruction of tubules (Ten Cate, 1998). The dentinal tubules in the mat dentine are approximately 3.2 um in diameter and are reduced to 1.0 um as they become increasingly calcified with age (Williams, 1985). As the tubules calcify, the refractive index of the fundamental matter becomes increasingly similar to the intratubular matrix (Drusini, 1989; Ten Cate, 1998). Eventually the dentine appears transparent in the transmitted light microscope. Secondary ion microscopy studies have shown that sclerosed tubules have a higher content of calcium, phosphorous, magnesium, potassium, and sodium than the intertubular dentine (Berkovitz, 1989).

The sclerosis usually begins at the root apex after an individual has reached the age of 20 and the teeth are fully erupted (Drusini, 1990; Ten Cate, 1998). The amount of sclerosis of the dentinal tubules is linearly correlated with age and is generally assumed to be unaffected by tooth function, pathology, or other external processes (Vasiliadis, 1983a). However, Johanson (1971) found some translucency in individuals between the ages of 15-19 years old. While, Lamendin (1992) reported that translucency was rarely present in individuals under the age of 30.

Another purpose of this study was to examine the relationship between tooth root color and age for Egyptian Nubians and its possible application in age assessment. The two general methods commonly used to analyze the natural color of teeth are visual comparison and instrumental measurements. Estimation of tooth root color by comparison with dental shade guides has been used extensively by forensic odontologists, since changes in color with aging have been described (Solheim, 1988; Solheim et al., 1993; Lackovic, 2000; Valenzuela et al., 2002). Nevertheless, the use of tooth color for age estimation in forensic odontology is limited because measurement is not objective in any visual-color matching procedure. In this study root color measurement obtained by computer image analysis have shown a significant correlation with chronological age. The estimated correlation between chronological age and RGB showed a strong linear relation with Spearman rho and Kendall tau correlation coefficients of -0.989 and -0.944 respectively. Similarly, correlation between chronological age and RGBI of color shows a strong linear relation with Spearman rho and Kendall tau correlation coefficients of -0.989 and -0.944 respectively. The results obtained in this study are in agreement with previously published results (Bhussry & Emmel, 1955; Biedow, 1963; Rheinwald, 1966; Tsuchiya, 1973; Ten Cate et al., 1977; Solheim, 1988; Lackovic & Wood, 2000).

Several researchers have noted that teeth become darker with age (Bhussry and Emmel 1955, Biedow 1963, Rheinwald 1966, Tsuchiya 1973, Ten Cate et al. 1977, Solheim 1988, Lackovic and Wood 2000). Color change has been noted in both enamel and dentin. Several factors have been attributed to color change noted in teeth, such as an increase in nitrogen in enamel (Bhussry and Emmel 1955), change in the refractive index between enamel and saliva, as a result from fracturing (de Jonge, 1950), and deposits of blood products in dentin (Rheinwald, 1966).

The change in color with age is due to continued cementum deposition through life is due to some undefined intrinsic change in the dentin. (Ten Cate et al., 1977; Solheim, 1988; Lackovic and Wood, 2000). The change in dental root color may be also due to the buildup of secondary dentine, itself darker than primary dentine, the greater transparency of primary dentine, and the adsorption of metallic ions into the surface of the enamel (Costa, 1986). Also cementum tends to get less permeable as age increases, this is thought to be due to a layer of calcium salts forming in the outermost layers of the cementum during later life (Jenkins,1966).

Although the assessment of color change using the traditional technique is a subjective technique, forensic odontologists have found it to be a reliable and useful method to estimate age-at-death (Ten Cate et al., 1977; Solheim , 1988; Lackovic and Wood, 2000). However a major criticism to the studies used the traditional methods of color description is related to the subjective manner in which the tooth color is assessed. This methodology ranks the color on a visual scale by mean of a descriptive standard. The visual scales have the flaw of lacking reproducibility in the assessment. To remedy this problem computer image assisted analysis was adopted for assessing tooth color. Consequently this technique establishes a methodology that is reproducible, objective and accurate.

The advantage of the colorimetric analysis using digital camera and Adobe Photoshop ® software used in this study lies in the fact that this method does not require a long and expensive laboratory preparation and can be performed with a minimal knowledge of computer skills. Linear regression equations had been driven to estimate the chronological age of the person with standard errors of ±2.76 and ± 3.76 for RGBI and RGB respectively. Similarly a regression equation had been driven for chronological age, RDT and RDTI. The standard errors of the estimates were ± 5.607 and ± 3.16 respectively.

A multivariate regression analysis had been performed to formulate a model of the relation between chronological age, RDT, RGB and RDTI, RGBI in conjunction. The standard errors of the estimates were ± 3.65 and ± 2.996 respectively.

The driven regression equations were validated by three different methods, namely application of the formulated regression equations to published data of known age, resampling technique and cross validation technique. The result for all the performed procedures points to the consistency and validity of the driven regression equations. Although the regression equations proved satisfactory accurate on application to samples of different background (published data) it is suggested that the validation should be done using samples driven from an ethnic background similar to the sample involved in this study (Egyptian-Nubians).

One other propose of this study was studying the effect of the sex on the rate of formation of root dentine translucency and dental root color was evaluated. Several researchers have found a significant difference between the sexes in root dentine translucency (Lorentsen and Solheim 1989, Prince and Ubelaker 2002), while others have not (Drusini et al. 1991, Lamendin et al. 1992). Lorentsen and Solheim (1989) suggested that sexual dimorphism in translucency may be attributed to differences in masticatory forces. Solheim and Sundnes (1980) compared results of age estimates obtained from three different methods namely: traditional macroscopic observations, intact tooth method of observing root translucency (Bang and Ramm 1970) and the Gustafson method (Dalitz 1963; Miles 1963; Johanson 1971) and found no significant differences in accuracy of estimates from pathological specimens, sex or tooth class.

As regard dental root color, Solheim (1988) analyzed 1000 extracted teeth of known-age, which ranged in age-at-extraction from 14-99 years. All tooth types except molars were represented in the sample, which consisted of 100 teeth from each tooth type, 50 from each side. Results of age estimation depending on dental root color showed no significant difference between the age estimates using the right or left sides, between chronological age and tooth age (age minus age at root completion of the tooth), reason for extraction, or between the sexes. Nevertheless there was a weak association between darkness of the tooth and post-mortem versus pre-mortem sampling which was significant for a number of different tooth types.

The results obtained from the statistical analysis of the present study are in accord with those reported by Solheim, 1988; Drusini et al., 1991 and Lamendin et al., 1992. The analysis showed lack of sexual dimorphism in both root dentine translucency and dental root color changes, as there was no statistical significant difference between males and females included in the study.

Good age indicator must possess certain criteria as repeatability, high accuracy, and high correlation with age. These features are critical when developing a biological profile, whether for forensic or paleodemographic purposes. Thus aging methods developed on indicators that are less susceptible to individual lifestyle aid in decreasing age ranges, especially for older individuals both root dentine translucency and dental color change proved in the present study to be a reliable age markers.

- Summary and conclusions

The sample used in this research consisted of 416 freshly extracted permanent teeth collected from 311 individual. Single rooted teeth maxillary and mandibular incisors, canines, and premolars were included in the study. In all cases, teeth were extracted from Nubian people belonging to one of four main Nubian groups (Kenuz, Arabs, Nubians (Fadija), and Halfans). The aim of the present study was to compare and evaluate two dental macroscopic age estimation criteria. Namely root dentine translucency (RDT) and dental root color measured as a mean value of the red, blue, and green components of color (RGB). RDT and RGB were assessed using traditional (manual) and computer image analysis quantification techniques.

The results of the present study suggest that both root dentine translucency and root color have a strong linear relation with chronological age. If the described preparation and analysis standard presented in this study are followed, the obtained age provides error estimates ranging from 2.7 to 5 years. Similarly the current research suggests that computer assisted image analysis techniques either in measuring root dentine translucency or analysis of color can accurately be used to estimate age at death . Such techniques are more accurate, reliable, reproducible and less liable to subjectivity of the observer. Moreover, the techniques do not require neither extensive dental histology knowledge, nor expensive laboratory equipments. The current research shows also that sex is not a contributing factor in root dentine translucency development, nor in dental root color changes.

The applications of the current study extend to different aspects of anthropology (Paleodemography and forensic anthropology). The application of RDT and RGB age estimations techniques help improves accurate age estimation. Such anaccuracy could enhance paleodemographic applications where it is crucial to obtain accurate individual age estimates, as accurate estimation of ages significantly affects the calculation of different demographic parameters.

As noted by several authors, all available skeletal age indicators should be adopted when possible. There are several important advantages to multiple-trait age estimates, because a more robust age estimate can be derived when multiple indicators corroborate an age range. In addition, interpersonal variation can be better understood when multiple indicators are analyzed. Focusing on only one or two age indicators will offer only a minimum understanding of the actual aging process.

Recommendations

- Future research should include analysis of large, known-aged samples of different ethnic backgrounds and from sites other than Nubia to assess effects of ethnic background on acquisition of translucency of the root and dental root color changes.

- In addition, further research should be done to reveal the underlying histological and physiological variation associated with change in dental root color and dental root translucency thus improve understanding of the relation of these phenomena with age.

- Future research should focus on building a totally automated age estimation system built upon image analysis which will increase the accuracy of age estimation and thus help in advancement of anthropological, demographical, forensics researches.

- Finally future research also should include analysis of large, known- aged forensic material to assess effects of taphonomic processes on acquisition of translucency of the root and dental root color changes. In addition, analysis of known-aged historical material will further enable comparison among statistical methodologies. As technological, methodological, and statistical advances add to the resources physical anthropologists employ to estimate age-at-death from dental and skeletal indicators, As resarchers will continually refine and improve techniques to more accurately establish a biological profile from skeletal remains.

- Abdi H (2007) Kendall rank correlation. In N.J. Salkind (Ed.): Encyclopedia of Measurement and Statistics. Thousand Oaks (CA): Sage.

- Acharya AB (2009) A new digital approach for measuring dentin translucency in forensic age estimation. Am J Forensic Med Pathol . 23(4):386-389

- Acsádi G, Nemeskéri J (1970) History of Human Life Span and Mortality. Budapest: Akadémiai Kiadó.

- Ajmal M, Mody B, Kumar G (2001) Age estimation using three established methods. A study on Indian population. Forensic Science International, 122:150-154.

- Altini M (1983) Age Determination from Teeth: A Review. Journ Dent Assocs Africa 38: 275 :279.

- Anderson M, Messner MB, Green WT (1964) Distribution of lengths of the normal femur and tibia in children from one to eighteen years of age. Journal of Bone and Joint Surgery 46:1197-1202

- Angel J, Suchey JM., Iscan MY and Zimmerman MR (1986) Age at death Estimated from the Skeleton and Viscera. In: Dating and Age Determination of Biological Materials. M.R. Zimmerman and J.L. Asgel, eds., 179-220. London: Croom Helm.

- Angel JL (1969) Bases of Palaeodemography. American Journal of Physical Anthropology 30:627-638.

- Angel JL (1984) Variation in estimating age at death of skeletons. Coll. Anthropol. 8:163-167.

- Ascadi G. & Nemeskeri J (1980) History of Human Lifespan and Mortality. Akademiai. Budapest.

- Aykroyd RG, Lucy D, Pollard AM (1996) Statistical methods for estimation of human age at death. Research Report No. STAT- 96/08(369K).

- Aykroyd RG, Lucy D, Pollard AM, Roberts CA (1990) Nasty, brutish, but necessarily short: A reconsideration of the statistical methods used to calibrate age at death from adult human skeletal and dental age indicators. American Antiquity, 64: 55-70.

- Aykroyd RG, Lucy D, Pollard AM, Solheim T (1997) Regression analysis in adult age estimation. American Journal of Physical Anthropology, 104: 259-265.

- Baccino E, Ubelaker DH, Hayek LA, Zerilli A (1999) Evaluation of seven methods of estimating age at death from mature human skeletal remains. Journal of Forensic Sciences, 44:931-6.

- Baccino E, Zerilli A (1997) The two step strategy (TSS) or the right way to combine a dental (Lamendin) and an anthropological (Suchey-Brooks System) method for age determination (abstract). Proceedings of the American Academy of Forensic Sciences, 150

- Ball J (2002) A critique of age estimation using attrition as the sole indicator. Journal of Forensic Odontostomatology, 20:38-42.

- Bang G, Ramm E (1970) Determination of age in humans from root dentin transparency. Acta Odontologica Scandinavica, 28:3-35.

- Bass WM (1987) Human Osteology: a laboratory and field manual. third edition. Missouri Archaeological Society. Columbia.

- Berkovitz BKB (1989) Teeth. New York: SpringerVerlag.

- Bhussry BR, Emmel V (1955) Changes in the nitrogen content of enamel with age. Journal of Dental Research, 34:627.

- Biedow J (1963) A modified method of age determination on teeth. Third International Meeting in Forensic Immunology, Medicine, Pathology, and Toxicology, Plenary Session IIA:37-38.

- Biggerstaff, RH (1977) Craniofacial characteristics as determinants of age, sex and race in forensic dentistry. Dental Clinics of North America, 21:85-97.

- Bittles AH and Collins KJ (1986) The Biology of Human Aging. Cambridge: Cambridge University Press.

- Boldsen JL, Milner GR, Konigsberg LW, Wood JW (2002) Transition analysis: A new method for estimating age from skeletons, in eds., Robert D. Hoppa & James W. Vaupel, Paleodemography: Age Distributions from Skeletal Samples, pp. 73-106, Cambridge University Press .

- Borrman H, Solheim T, Magnusson B, Kvaal SI, St ene-Johans en W (1995) Inter examiner variation in the assessment of age-related factors in teeth. International Journal of Legal Medicine, 107:183-186.

- Bosmans N, Ann P, Aly M, Willems G (2005) The application of Kvaal’s dental age calculations technique on panoramic dental radiographs. Forensic Sci Int 153:208-212

- Brooks ST (1955) Skeletal age at death: The reliability of cranial and pubic age indicators. American Journal of Physical Anthropology, 13:567-597.

- Brooks ST, Suchey JM (1990) Skeletal age determination based on the os pubis: A comparison of the Acsádi-Nemeskéri and Suchey-Brooks methods. Journal of Human Evolution, 5:227-238.

- Brothwell D (1963) Digging Up Bones. London, British Museum (Natural History).

- Brothwell D (1989) The relationship of tooth wear to aging. In MY Ypcan (ed.) Age Markers in the Human Skeleton. Charles C Thomas, Springfield, Illinois, 303-316.

- Buckberry JL, Chamberlain AT (2002) Age estimation from the auricular surface of the ilium: a revised method. American Journal of Physical

Anthropology, 119:231-9.

- Buikstra JE, Ubelaker DH (1994) Standards for the data collection from human skeletal remains. Research series 44. Fayetteville: Arkansas Archeological Survey.

- Cameriere R, Ferrante L, De Angelis D et al (2008) The comparison between measurement of open apices of third molars and Demirjian stages to test chronological age of over 18 year olds in living subjects. Int JLegal Med 122(6):493-497

- Landa MI, Garamendi PM, Botella MC et al (2009) Application of the method of Kvaal et al. to digital orthopantomograms. Int J Legal Med 123(2):123-128

- Carolan VA, Gardner ML, Lucy D, Pollard AM (1997) Some considerations regarding the use of amino acid racemization in human dentine as an indicator of age at death. Journal of Forensic Sciences, 42:10-16.

- Chakravarti L , Roy A (1967) Handbook of Methods of Applied Statistics, John Wiley and Sons, 1: 392-394.

- Charles DK, Condon K, Cheverud JM, Buikstra JE (1986) Cementum annulation and age determination in Homo sapiens: I. Tooth variability and observer error. American Journal of Physical Anthropology, 71:311­320.

- Chernick MR (1999) Bootstrap Methods, A practitioner's guide. Wiley Series in Probability and Statistics.

- Colonna M, Introna F, F avia G, Pesce-Delfino V (1984) Valutazione della transparenza della dentina per la determinazione dell’eta: revisione metodologica e analisi di un campione. In F deFazio and B Vernole (eds.), La Laurea in Odontoiatria e Protesti dentaria. I Problemi Medico-

legali in Odontostomatologia. CIC Edizioni Internazionali, 357-368.

- Condon K, Charles DK, Cheverud JM, Buikstra JE (1986) Cementum annulation and age determination in Homo sapiens: II. Estimates and accuracy. American Journal of Physical Anthropology, 71:321-330.

- Conover WJ (1999) Practical Nonparametric Statistics (3rd edition). Wiley.

- Cook R (1977) Detection of Influential Observations in Linear Regression. Technometrics (American Statistical Association) 1:15-18.

- Cook R (1979) Influential Observations in Linear Regression. Journal of the American Statistical Association (American Statistical Association) 74 365: 169-174.

- Costa RL (1986) Determination of age at death: dentition analysis. In: Dating and Age Determination of Biological Materials. (eds. Zimmerman, MR & Angel JL ) 248-269

- Cronbach L (1951) Coefficient Alpha and the Internal Structure of Tests. Psychometrika ;16(3):297-333, 1951.

- Cross JF, Kerr NW, Bruce MF (1986) An evaluation of Scott’s method for scoring dental wear. In E Cruwys and RA Foley (eds.) Teeth and Anthropology. BAR International Series No 291, Oxford: British Archaeological Reports,101-108.

- Currey JD (1964) Some Effects of Aging in Human Haversian Systems. Journal of Anatomy, 98:69-75.

- Dahl BL, Oilo G, Anderson A, Bruaset O (1989) The suitability of a new index for the evaluation of dental wear. Acta Odontologica Scandinavica, 47:205-210.

- Davison AC, Hinkley D (2006) Bootstrap Methods and their Applications. Bootstrap Methods and their Applications (8th ed.). Cambridge:

Cambridge Series in Statistical and Probabilistic Mathematics.

- De las Heras SM, Renzo AV, Bellini C, Salas C, Rubino M, Garcia (2003) Objective measurement of dental color for age estimation by spectroradiometry, Forensic Science International 132: 57-62.

- Dechaume M, Derobert L, Payen J (1960) De la valeur de la determination de l’agepar examen des dentes en coupes minces. Annales de Medicine Legale, Paris, 40:165-167.

- Dreier FG (1994) Age at death estimates of the protohistoric Arikara using molar attrition rates: a new quantification method. International Journal of Osteoarchaeology, 4:137-148.

- Drusini A, Businaro F and Volpe A (1989) Age Determination from Root Dentine Transparency of Intact Human Teeth, Cahiers d'Anthropologie et Biometrie Humaine (Paris) VII (12):109 127.

- Drusini A, Calliari I, Volpe A (1991) Root dentine transparency: age determination of human teeth using computerized denstiometric analysis. Am JPhys Anthropol 85:25-30

- Drusini A, Callieari I and Volpe A (1991) Root dentine transparency: age determination of human teeth using computerized densitometric analysis. American Journal of Physical Anthropology, 85:25-30.

- Drusini A, Volpe A and Dovigo S (1990) Age Determination in Human Adults by Dental Histology. Morph. Anthrop. 78 (2): 169-174.

- Frost HM (1980) Skeletal Physiology and Bone Remodeling. In: Fundamental .and Clinical Bone Physiology. M.R. Urist, ed.,. Philadelphia: JB. Lippincott Company 208-241

- Garci'a JA , Rubin~o M, Romero J, Hita E (1993) Measuring the whiteness of human teeth, Color Res. Appl. 18: 349-352.

- Gegauff AG, Rosenstiel SF, Langhout KJ, Johnston WM (1993)

Evaluating tooth color change from carbamide peroxide gel, JADA 12:4 65-72.

- Gilbert BM (1973) Misapplication to females of the standard for aging the male os pubis. American Journal of Physical Anthropology 38:39-40.

- Gilbert BM, McKern TW(1973) A method of aging the female os pubis. American Journal of Physical Anthropology, 38:31-38.

- Good P (2005) Introduction to statistics through Resampling Methods andR/S-PLUS. Wiley.

- Good P (2006) Resampling Methods. 3rd Ed. Birkhauser.

- Goodkind R.J., Schwabacher W.B., Use of a fiber-optic colorimeter for in vivo color measurements of 2830 anterior teeth, J. Prosthet. Dent. 58 535-541,1987.

- Goodkind RJ, Keenan KM, Schwabacher WB (1985) A comparison of Chromascan and spectrophotometric color measurements of 100 natural teeth, J Prosthet Dent 105-109.

- Gustafson G (1947) Âldersbestàmnigarpa tänder. Odontologisk Tidskrift, 54:556-568.

- Gustafson G (1955) Altersbestimmungen an Zähnen. Deutsche Zahnärztliche eitschrift,10:1763-1768.

- Gustafson, G (1950) Age determination on teeth. Journal of the American Dental Association, 41:45-54.

- Hanihara K (1952) Age changes in the male Japanese pubic bone. Journal of the Anthropological Society of Nippon, 62:245-260.

- Harper GJ, Crews DE (2000) Aging, senescence, and human variation. In: Stinson, S., Bogin, B., Huss-Ashmore, R., O’Rourke, D., eds., Human Biology: an Evolutionary and Biocultural Perspective. Wiley-Liss, New York, NY, 465-505.

- Helfman PM, Bada J (1975) Aspartic acid racemization in enamel from living humans. Proceedings of the National Academy of Science, USA, 72:2891-2894, 1975.

- Helfman PM, Bada J (1976) Aspartic acid racemization in dentine as a measure of ageing. Nature, 262:279-281.

- Helm S, Prydso U (1979) Assessment of age-at-death from mandibular molar attrition in medieval Danes. Scandinavian Journal of Dental Research, 87:79-90.

- Hillson S (1986a) Teeth, age, growth and archaeology. In: Science in Egyptology. (ed. David AR). Manchester University Press. Manchester.

- Hillson S (1986b) Teeth. Cambridge University Press. Cambridge.

- Hillison S (1996) Dental Anthropology, Cambridge University Press, England.

- Hindle JP, Harrison A (2000) Tooth colour analysis by a new optoelectronic system, Eur. J. Prosthodont. Restor. Dent. 857-61.

- Hollander M, Wolfe DA (1973) Nonparametric statistical methods, Wiley.

- Hoppa RD (2002) Paleodemography: looking back and thinking ahead. In, Robert D. Hoppa & James W. Vaupel (eds.), Paleodemography: Age Distributions from Skeletal Samples. Cambridge University Press, 9-28,

- Hoppa RD, Vaupel JW (2002a) Paleodemography: Age Distributions from Skeletal Samples. Cambridge University Press.

- Hoppa RD, Vaupel JW (2002b) The Rostock Manifesto for paleodemography: the way from stage to age, In, Robert D. Hoppa & James W. Vaupel (eds.), Paleodemography: Age Distributions from Skeletal Samples. Cambridge University Press, 1-8 .

- Hughes DR (1968) Skeletal Plasticity and its Relevance in the Study of Earlier Populations. In: The Skeletal Biology of Earlier Human

Populations. D.R. Brothwell, ed., Symposia of the Society for the Study of Human Biology, Oxford: Pergamon Press, 8:31-55

- Hunt RWG (2004) The Reproduction of Colour , 6th ed. Chichester UK: Wiley-IS&TSeries in Imaging Science and Technology.

- Igbigbi PS, Nyirenda SK (2005) Age estimation of Malawian adults from dental radiographics. West Afr J Med 24:329-333

- Iscan MY, Loth SR, Wright RK (1984a) Metamorphosis at the sternal rib end: A new method to estimate age at death in white males. American Journal of Physical Anthropology, 65:147-156.

- Iscan M, Loth SR & Wright RK (1984b) Age determination from the ribs by phase analysis: White males. Journal of Forensic Sciences. 29:1094­1104.

- Iscan M, Loth SR & Wright RK (1985) Age estimation from the ribs by phase analysis: White females. Journal of Forensic Sciences. 30:853-863.

- Iscan MY, Loth SR (1986) Determination of age from the sternal rib in white females: A test of the phase method. Journal of Forensic Sciences, 31:990­999.

- Iscan MY, Loth ST, Wright RK (1987) Racial variation in the sternal extremity of the rib and its effects on age determination. Journal of Forensic Sciences, 32:452-466.

- Jackes M (1985) Pubic symphysis age distributions. Am. J. Phys. Anthropol., 68:281-299.

- Ito S (1972) Research on age estimation based on teeth. Japanese Journal of Legal Medicine, 26:31-41,1972.

- Ito S (1975) Age estimation based on tooth crowns. International Journal of Forensic Dentistry, 3:9-14 .

- Itten J (1990) The Elements of Color, Springer.

- Jaches M (1993) On paradox and osteology. Current Anthropology, 34:434-439.

- Jaches M (2000) Building the bases for paleodemographic analysis: Adult age Determination. In AM Katzenberg & SR Saunders (eds.), Biological Anthropology of the Human Skeleton, Wiley- Liss, Inc., 417-466.

- Jenkins NG (1966) The Physiology of the Mouth. Blackwell Scientific Publications 3rd edition, Oxford.

- Johanson G (1971) Age determination from human teeth. Odontologish Revy, 22:1-126.

- Johansson A, Haraldson T, Omar R, Kiliaridis S, Carlsson GE (1993) A system for assessing the severity and progression of occlusal tooth wear. Journal of Oral Rehabilitation, 20:125-131.

- Johnson CC (1968) Transparent dentine in age estimation. Oral Surgery, 25:834-838.

- Kani M (1954) On the increase of the average specific gravity of teeth with increase with age. Ichikawa Gahuho. 2 :8-13.

- Kashyap VK, Koteswara Rao NR (1990) A modified Gustafson method of age estimation from teeth. Forensic Sci Int 47(3):237- 247.

- Kato K (1956) Hardness of the teeth. Rinsho Shika. 213:4

- Katz D, Suchey JM (1986) Age determination of the male os pubis. Am. J. Phys. Anthropol., 69:427-435.

- Katz D, Suchey JM (1989) Races differences in pubic symphyseal aging patterns in the male. Am. J. Phys. Anthropol., 80:167-172.

- Kemkes-Grottenthaler(1996) A Critical evaluation of osteomorphognostic methods to estimate adult age at death: a test of the “complex method”. Homo, 46:280-292 .

- Kemkes-Grottenthaler(2002) Aging through the ages: historical perspectives on age indicator methods. In, Robert D. Hoppa & James W. Vaupel (eds.), Paleodemography: Age Distributions from Skeletal Samples. Cambridge University Press, 48-72.

- Kendall M (1938) A New Measure of Rank Correlation, Biometrika, 30: 81-89.

- Kendall M (1962) Rank correlation methods, Griffin.

- Kendall M (1948) Rank Correlation Methods, Charles Griffin & Company Limited.

- Kerley ER (1970) Estimation of Skeletal Age: After About Age 30. In: Personal Identification in Mass Disasters. T.D. Stewart, ed., Washington: National Museum of Natural History, Smithonian Institution, 57-70.

- Kim YK, Kho HS, Lee KH (1995) Age estimation by occlusal tooth wear. Journal of Forensic Science, 45:303-309.

- Klatsky M, Fisher RL (1953) The human masticatory apparatus: An in­troduction to dental anthropology. Brooklyn: dental items of interest Publishing Co.

- Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection". Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, Morgan Kaufmann, San Mateo, 2: 137-1143.

- Koingsberg LW, Frankenberg SR (1994) Paleodemography: “Not quite dead.” Evolutionary Anthropology, 3:92-105.

- Komer DK (2003) Twenty-seven years of forensic anthropology casework in New Mexico. Journal of Forensic Science, 48:521-524.

- Konigsberg LW, Frankenberg SR (1992) Estimation of age structure in anthropological demography. Am. J. Phys. Anthropol., 89:235-256.

- Kruskal WH (1958) Ordinal Measures of Association, Journal of the American Statistical Association, 53: 814-861.

- Kvaal S, Solheim T (1994) A non-destructive dental method for age estimation. Journal of Forensic Odontostomatology,12:6-11.

- Kvaal S, Kolltveit KM, Thomsen IO, Solheim T (1995a) Age estimation of adults from dental radiographs. Forensic Science International, 74:175­185,

- Kvaal S, Solheim T (1995b) Incremental lines in human dental cementum in relation to age. European Journal of Oral Science, 103:225-230 .

- Kwak KW & Kim CY (1993) Comparative study of age estimation accuracy in Gustafson’s method and prediction formula by multiple regressions. Journal of Forensic Odontostomatol. 10 :43-48, 1993

- Lackovic KP, Wood RE (2000) Tooth root colour as a measure of chronological age. The Journal of Forensic Odonto-Stomatology, 18:37­45.

- Lamendin H (1988) Determination de l’age avec la méthode de Guftason “simplifiée” [in French] Chir. Dent. Fr. 58:43-47.

- Lamendin H, Cambray JC (1980) Etude de la translucidite et des canalicules dentinaires pour appreciation de l’age. Societe de Medecine Legale, 9:489-499.

- Lamendin H, Baccino E, Humbert JF, Tavernier JC, Nossintchouk RM, Zerilli A (1992) A simple technique for age estimation in adult corpses: The two criteria dental method .Journal of Forensic Sciences, 37:1373­1379.

- Lantelme RL, Handelman SL, Herbison RJ (1976) Dentin formation in periodontally diseased teeth. Journal of Dental Research, 55:48-51.

- Larson CS (1997) Bioarchaeology: Interpreting Behavior from the Human Skeleton. Cambridge Studies in Biological Anthropology 21. Cambridge: Cambridge University Press.

- Laskarin M, Brkis H, Pichler G ,Bukovis D (2006) Tooth Root Colour as an Indicator of Age, Coll. Antropol. 30 4: 807-810.

- Lavelle CLB (1970) Analysis of attrition in adult human molars. Journal of Dental Research, 49:822- 828.

- Li C, Ji G (1995) Age estimation from the permanent molar in northeast China by the method of average stage of attrition. Forensic Science International, 75:189-96.

- Logan WHG, and Kronfeld R (1933) Development of the Human Jaws and Surrounding Structures from Birth to the Age of Fifteen Years, J. Amer. dent. Ass., 20:379.

- López-Nicolás M, Morales A, Luna A (1993) Morphometric study of teeth in age calculation. Journal of Forensic Odontostomatology, 11:1-8.

- López-Nicolás M, Morales A, Luna A (1996) Application of dimorphism in teeth to age calculation. Journal of Forensic Odontostomatology, 14:9­12.

- Lorensten H, Solheim T (1989) Age assessment based on translucent dentine. Journal of Forensic Odontostomatology, 7:3-9.

- Lorenz FO (1987) Teaching about Influence in Simple Regression, Teaching Sociology (American Sociological Association) 15: 173-177.

- Lovejoy CO (1985) Dental wear in the Libben population: its functional pattern and role in the determination of adult skeletal age at death. American Journal of Physical Anthropology, 68:47-56.

- Lovejoy CO, Meindl RS, Mensforth RP & Barton TJ (1985a) Multifactorial determination of skeletal age at death: a method and blind tests of its accuracy. American Journal of Physical Anthropology, 68:1­14.

- Lovejoy C, Meindl RS, Pryzbeck TR & Mensforth RP (1985b) Chronological metamorphosis of the auricular surface of the ilium: a new method for the determination of adult skeletal age. American Journal of Physical Anthropology. 68:15-28.

- Lucy D and Pollard AM (1995a) Further comments on the estimation of error associated with the Gustafson’s dental age estimation method. Journal of Forensic Science. Journal of Forensic Science. 40 (2): 227-7.

- Lucy D, Pollard AM (1995c) .Further comments on the estimation of error associated with the Gustafson dental age estimation Method. J. Forensic Sci. 40:222-227.

- Lucy D, Pollard AM and Roberts CA (1995b) A comparison of three dental techniques for estimating age at death in humans. Journal of Archaeological Science; 22: 417-428.

- Lucy D, Aykroyd RG, Pollard AM, Solheim T (1996) A Bayasian approach to adult human age estimation from dental observation by Johanson’s age changes.;41(2):189-94.

- Lunneborg C (1999) Data Analysis by resampling, Duxbury Press.

- Lunt DA (1978) Analysis of attrition in adult human molars. In PB Butler and J Joysey (eds.), Development, Function, and Evolution of Teeth, Academic Press: London, 465-482.

- Manly RS, Hodge HC (1939) Dentistry and refractive index studies of dental hard tissues I. Journal of Dental Research, 18:133-141.

- Maples WR (1978) An improved technique using dental histology for estimation of adult age. Journal of Forensic Sciences, 764-770.

- Maples WR, Rices PM (1979) Some difficulties in the Gustafson dental age estimations. J. Forensic Sci. 24:168-172.

- Marcsik A, Kósa F, Kocsis G (1992) The possibility of age determination on the basis of dental transparency in historical anthropology. In, Patricia Smith & Eitan Tchernov (eds.), Structure, Function and Evolution of Teeth, Freud Publishing House Ltd, 527-538.

- Masters PM (1986) Age at death determinations for autopsied remains based on aspartic acid racemization in tooth dentin: importance of post­mortem conditions. Forensic Science International, 32:179-184.

- Matsikidis G, Schultz P (1982) Altersbestimmung nach dem Gebiss mit Hilfe des Zahnfilms. Zahnaerztliche Mitteilungen, 72:2527-2528.

- McKern, TW (1970) Estimation of skeletal age: From Puberty to About 30 Years of Age. In: Personal Identification in Mass Disasters. T.D. Stewart ed. Washington: National Museum of Natural History, Smithonian Institution. , 1970.41-56

- McKern TW, Stewart TD (1975) Skeletal age changes in young American males.Natick, MA Quartermaster Research and Development Command, Technical Report EP.45.

- Meindl RS, Lovejoy CM, Mensforth RM, Walker RA (1985) A revised method of age determination using the os pubis, with a review and tests of accuracy of other current methods of pubic symphyseal aging. American Journal of Physical Anthropology, 68:29-46.

- Meinl A, Tangl S, Pernicka E, Fenes C, Watzek G (2007) On the applicability of secondary dentin formation to radiological age estimation in young adults. J Forensic Sci 52:438-441

- Metzger Z, Buchner A, Gorsky M (1980) Gustafson’s method for age determination from teeth - a modification for the use of dentists in identification teams. Journal of Forensic Science, 25:742-749.

- Miles AEW (1962) Assessment of the ages of a population of Anglo­Saxons from their dentitions. Proceedings of the Royal Society of

Medicine, 55:881-886.

- Miles AEW (1963) Dentition and the estimation of age. Journal of Dental Research, 42:255-263.

- Miles AEW (1978) Teeth as an indicator of age in man. In PM Butler and KA Joysey (eds.), Development, Function and the Evolution of Teeth, London: Academic Press, 455-462.

- Molnar S (1971) Human tooth wear, tooth function and cultural variability. American Journal of Physical Anthropology, 34:175-190.

- Montagu MFA (1938) Aging of the skull. American Journal of Physical Anthropology, 23:355-375.

- Monzavi BF, Ghodoosi A, Savabi O, Hasanzadeh A (2003) Model of age estimation based on dental factors of unknown cadavers among Iranians. Journal of Forensic Sciences, 48:1-3.

- Moore GE (1970) Age changes occurring in the teeth. Journal of the Forensic Sciences Society, 10:179-180.

- Moore-Jansen PH, Jantz RL (1986) A Computerized Skeletal Data Bank for Forensic Anthropology. Knoxville, TN: Department of Anthropology, University of Tennessee.

- Morse DR, Esposito JV, Schoor RS (1993) A radiographic study of aging changes of the dental pulp and dentin in normal teeth. Quintessence International, 24:329-333.

- Murphy T (1959) Gradients of dental exposure in human molar tooth attrition. American Journal of Physical Anthropology, 17:179-186.

- Murray KA, Murray T (1991) A test of the auricular surface aging technique. J. Forensic Sci. 36:1162-1169, 1991.

- Nalbandian J and Soggnaes RF (1960) Structural Age Changes in Human Teeth.

- Nalbandian J, Gonzales F& Sognnaes RF (1960) Sclerotic age changes in root dentine of human teeth as observed by optical, electron, and Xray microscopy. Journal of Dental Research. 39:598-1960.

- Nemeskéri J, Harsányi L, Acsádi G (1960) Methoden zur diagnose des Lebensalters von skelettfunden. Anthropologischer Anzeiger, 24:70-95

- Nkhumeleni FS , Raubenheimer E, Monteith BD (1989) Gustafson’s method for age determination revised. J. Forensic Odontostomatol. 7:13­16.

- Ogino T, Ogino N, Nagy B (1985) Application of aspartic acid racemization to forensic odontology: postmortem designation of age at death. Forensic Science International, 29:259-267.

- Ohtani S (1995) Studies on age estimation using racemization of aspartic acid in cementum. Journal of Forensic Sciences, 40:805-807.

- Olze A, Geserick G, Schmeling A (2004) Age estimation of unidentified corpses by measurement of root translucency. J Forensic Odontostomatol 22(2):28-33

- Olze A, van Nierkerk P, Ishikawa T, Zhu BL, Schulz R, Maeda H, Schmeling A (2007) Comparative study on the effect of ethnicity on wisdom tooth eruption. Int J Legal Med 121:445-448

- Ortner DJ & Putschar WGJ (1981) Identification of Pathological Conditions in Human Skeletal Remains. Smithsonian Institution Press: Smithsonian Contributions to Anthropology, Number 28. Washington.

- Paine RR (1997) Uniformitarian Models in Osteological Paleodemography in Integrating Archaeological Demography: Multidisciplinary Approaches to Prehistoric Population. Carbondale : Center for Archaeological Investigations, Southern Illinois University, Visiting Scholar Conference v. 24: 191-204.

- Pedersen PO, Dahlberg AA., Alex-andersen V (1967) Proceedings of the international symposium on dental morphology. J. Dent. Res. 46 : 75-76

- Pillai PS & Bhaskar G (1974) Age Estimation from the teeth using Gustafson’s method - A Study in India. Journal of Forensic Science: 3: 135-141.

- Poynton CA (2003) Digital Video and HDTV: Algorithms and Interfaces. Morgan Kaufmann

- Prince DA (2004) Estimation of adult age at death using root dentine translucency, Phd. Thesis, University of Tennessee, Knoxville .

- Prince DA, Ubelaker DH (2002) Application of Lamendin’s adult dental aging technique to a diverse skeletal sample. Journal of Forensic Science, 47:107-116 .

- Prince DA, Konigsberg LW (2004) New formulae for estimating age in the Balkans utilizing Lamendin’s dental technique. Proceedings of the 56th Annual American Academy of Forensic Sciences, 16-21 February 2003, Dallas, TX. Pp.279.

- Rathod H, Roberts D, Gray C, Jones SJ, Boy de A (1993) Autofluorescence mode confocal microscopy of dental tissues. Proceedings of Bone and Tooth Society, 1:39.

- Reppien K, Sejrsen B, Lynnerup N (2006) Evaluation of postmortem estimated dental age versus real age: a retrospective 21-year survey. Forensic Sci Int 159(1 Suppl):S84-S88

- Richard Scott G. and Christy G. Turner II (1988) Dental anthropology. Annual Review of Anthropology, 17: 99-126.

- Ritz S, Schütz HW, Schwatzer B (1990) The extent of aspartic acid racemization in dentin: a possible method for a more accurate determination of age at death? Zeitschrift für Rechtsmedizin, 103:457- 462.

- Rose MR (1991) Evolutionary Biology of Aging. Oxford University Press, New York, NY.

- Rosentiel SF, Gegauff AG, McCafferty RJ, Johnston WM (1991) In vitro tooth color change with repeated bleaching, Quintessence Int. 22 7-12.

- Rösing FW, Kvaal SI (1997) Dental age in adults. A review of estimation methods, [in:] Dental anthropology. Fundamentals, limits and prospects, Alt K. W., Rösing F. W., Teschler-Nicola M. [eds.], Springer, Wien.

- Ross AH, Konigsberg LW (2002) New formulae for estimating stature in the Balkans. Journal of Forensic Science; 47:165-167.

- Royston JP (1982) The W Test for Normality. Applied Statistics;31(2):176-180.

- Royston JP (1995) Shapiro-Wilk normality test and P-value. Applied Statistics;44(4).

- Rubino M,Garcia JA, Jimenez del Barco L., Romero J (1994) Colour measurement of human teeth and evaluation of a colour guide, Color Res. Appl. 19: 19-22.

- Russell KF (1996) Determination of Age-at-Death from Dental Remains. Ph.D. Dissertation, Kent State University.

- Saglam Atsu S, Sema Aka P, Ibrahim N (2006) D§lerin Kok §effafhgndan Ya§ Tespiti (Age estimation from root transparency). Turkiye Klinikleri J Dental Sci, 12 :46-52.(Paper in Turkish with English abstract).

- Salo k (2005) What Ancient Human Teeth Can Reveal? Demography, Health, Nutrition and Biological Relations in Luistari , Master degree thesis, department of archaeology , University of Helsinki

- Sarajliæ N, Klonowski EE, Drukier P, Harrington R (2003) Lamendin’s and Prince’s dental aging methods applied to a Bosnian population. Proceedings of the Fifty-fourth Annual American Academy of Forensic Sciences 6:239-240. (abstract),.

- Saunders SR, Fitzgerald C, Rogers T, Dudar C, McKillop H (1992) A test of several methods of skeletal age estimation using a documented archaeological sample. Can. Soc. Forensic Sci. 25:97-118.

- Schmeling A, Geserick G, Reisinger W et al (2007) Age estimation. Forensic Sci Int 165(2-3):178-181

- Schmeling A, Grundmann C, Fuhrmann A et al (2008) Criteria for age estimation in living individuals. Int JLegal Med 122(6):457- 460

- Schmidt S, Nitz I, Schulz R et al (2008) Applicability of the skeletal age determination method of Tanner and Whitehouse for forensic age diagnostics. Int J Legal Med 122(4):309-314

- Schmitt A (2004) Age at death assessment using the os pubis and the auricular surface of the ilium: a test on an identified Asian sample. Int. J. Osteoarcheol. 14:1-6.

- Schmitt A, Murail P (2004) Is the first rib a reliable age indicator of age at death assessment? Test of the method elaborated by Kunos et al. (1999). Homo. 54:207-214.

- Schour I & Massler M (1941) The Development of the Human Dentition, J. Amer. dent. Ass., 28: 11-53.

- Schulz R, Mühler M, Reisinger W, Schmidt S, Schmeling A (2008a) Radiographic staging of ossification of the medial clavicular epiphysis. Int J Legal Med 122:55-58

- Schulz R, Zwiesigk P, Schiborr M, Schmidt S, Schmeling A (2008b) Ultrasound studies on the time course of clavicular ossification. Int J Legal Med 122:163-167

- Scott EC (1979) Dental wear scoring technique. American Journal of Physical Anthropology, 51:213-218.

- Sengupta A, Shellis RP, Whittaker DK (1998) Measuring root dentine translucency in human teeth of varying antiquity. Journal of Archaeological Science, 25:1221-1229.

- Sengupta A, Whittaker DK, Shellis RP (1999) Difficulties in estimating age using root dentine translucency in human teeth of varying antiquity. Archives of Oral Biology,44:889-899.

- Shaaban M (1988) Paleodemography of a Pre-Roman population from El-Dakhleh, Egypt: evidence from the skeletal remains at Site 31/435-D5- 2, PhD thesis University of Toronto.

- Shapiro SS, Wilk MB (1965) An analysis of variance test for normality. Biometrika ; 52:591-9.

- Shimoyama A, Harada K (1984) An age determination of an ancient burial mound man by apparent racemization reaction of aspartic acid in tooth dentin. Chemistry Letters, 1984:1661-1664.

- Singer R (1953) Estimation of age from cranial suture closure. Journal of Forensic Medicine, 1:52-59.

- Slaus M, Strinoviæ D, Skaviæ J, Petroveèki V (2003) Discriminate function sexing of fragmentary and complete femora: Standards for contemporary Croatia. Journal of Forensic Science, 48:509-512.

- Smith BH (1948a) Patterns of molar wear in hunter-gatherers and agriculturalists. American Journal of Physical Anthropology, 63:39-56.

- Smith BH (1948b) Rate of molar wear: implications for developmental timing and demography in human evolution. American Journal of Physical Anthropology, 63:220.

- Smith P (1972) Diet and attrition in the Natufians. American Journal of Physical Anthropology, 37:233-238.

- Sognnaes RF, Gratt BM, Papin PJ (1985) Biomedical image processing for age measurements of intact teeth. Journal of Forensic Sciences, 30:1082-1089.

- Solheim T, Sundnes PK (1980) Dental age estimation of Norwegian adults -A comparison of different methods. Forensic Science International, 16:7-17

- Solheim T( 1984) Dental age estimation. An alternative technique for tooth sectioning. American Journal of Forensic Medicine and Pathology, 5:181-184.

- Solheim T (1988a) Dental attrition as an indicator of age. Gerodontics, 4:299-304.

- Solheim T (1988b) Dental color as an indicator of age, Scand. J. Dent. Res. 97189-197.

- Solhiem T (1989) Dental root translucency as an indicator of age. Scandinavian Journal of Dental Research, 97:189-97.

- Solhiem T (1990) Dental cementum apposition as an indicator of age. Scandinavian Journal of Dental Research, 98:510-519.

- Solhiem T, Kvaal S (1993) Dental root surface structure as an indicator of age. Journal of Forensic Odontostomatology, 11:9-21.

- Solheim T (1992) Recession of periodontal ligament as an indicator of age. Journal of Forensic Odontostomatology, 19:32-42.

- Solheim T (1993) A new method for dental age estimation in adults. Forensic Science International, 59:137-147.

- Solheim T, Vonen A (2006) Dental age estimation, quality assurance and age estimation of asylum seekers in Norway. Forensic Sci Int 159(1 Suppl):S56-S60

- Song HW, Jai JT (1989) The estimation of tooth age from attrition of the occlusal surface. Medicine, Science and the Law 1:69-73.

- Soomer H, Ranta H, Lincoln M J, Pentilla A, Leibur E (2003) Reliability and validity of eight dental age estimation methods for adults used in forensic odontology. J. Forensic Sci. 48:149-152.

- Sopher IM (1976) Forensic Dentistry, Charles C. Thomas, Springfield.

- Spearman A (1904) The proof and measurement of association between two things Amer. J. Psychol., 15 : 72-101.

- Stephens MA (1974) Statistics for Goodness of Fit and Some Comparisons, Journal of the American Statistical Association, 69:730­737.

- Stewart TD (1979) Essentials of Forensic Anthropology. Springfield: Charles C Thomas Publisher.

- Suchey JM (1986) Problems in the aging of females using the Os pubis. American Journal of Physical Anthropology, 51:467-470.

- Ten Cate AR, Thompson GW, Dickinson JB, Hunter HA (1977) The estimation of age of skeletal remains from colour of roots of teeth. Journal of the Canadian Dental Association, 43:83-86,1977.

- Ten Cate AR (1998) Oral Histology: development, structure and function. Missouri: Mosby.

- Theroux A( 1994) The Primary Colors. New York: Henry Holt.

- Theroux A (1996) The Secondary Colors. New York: Henry Holt

- Thomas GJ, Whittaker DK, Embery G (1994) A comparative study of translucent apical dentine in vital and non-vital human teeth. Archives of Oral Biology, 39:29-34.

- Todd TW (1920) Age changes in the pubic bone. I. The male white pubis. American Journal of Physical Anthropology, 3:285-334.

- Todd TW (1921a) Age changes in the pubic bones. II. The pubis of the male Negro-white hybrid. III. The pubis of the white female. IV. The pubis of the female Negro-white hybrid. American Journal of Physical Anthropology, 4:4-70.

- Todd TW (1921b) Age changes in the pubic bones. V. Mammalian pubic bone metamorphosis. American Journal of Physical Anthropology, 4:333­406.

- Ubelaker DH, Parra RC (2008) Application of three dental methods of adult age estimation from intact single rooted teeth to a Peruvian sample. JForensic Sci 53(3):608-611

- Valenzuela A, Martin-De Las Heras S, Mandojana JM, De Dios Luna J, Valenzuela M and Villanueva E (2002) Multiple regression models for age estimation by assessment of morphologic dental changes according to teeth source. American Journal of Forensic Med. Pathol. 23 : 386-9.

- Vasiliadis L, Darling AI, Levers BGH (1983a) The amount and distribution of sclerotic human root dentine. Archives of Oral Biology, 28:645-649.

- Vasiliadis L, Darling AI, Levers BGH (1983b) The histology of sclerotic root dentine. Archives of Oral Biology, 28:693-700.

- Vlèek E, Mrklas L (1975) Modification of the Gustafson method of determination of age according to teeth on prehistorical and historical osteological material. Scripta Medica; 48:203-208.

- Walker RA, Lovejoy CO (1985) Radiographic changes in the clavicle and proximal femur and their use in the determination of skeletal age at death. American Journal of Physical Anthropology, 68:67-78.

- Wegener R (1980) Zur Schäitzung des Alters anhand der Zahnwurzel transparenz (Estimation of Age from Root Dentine Transparency ) Z Rechtsmed 86: 29-34.

- Whittaker DK (1982) Research in forensic odontology. Journal of Royal College of Surgeons of England. 64: 175-180.

- Whittaker DK, Bakri MM (1996) Racial variations in the extent of tooth root translucency in ageing individuals. Archives of Oral Biology, 41:15­19.

- Willems G, Moulin-Romsee C, Solheim T (2002) Non-destructive dental- age calculation methods in adults: intra- and inter-observer effects. Forensic Sci. Int. 126:221- 226.

- Wittwer-Backofen U, Gampe J, Vaupel JW (2004) Tooth cementum annulation for age estimation: results from a large known-age validation study. American Journal of Physical Anthropology,123:119-129.

- Wood JW, Holman DJ, O'Connor K, Ferrell RJ (2002) Mortality models for paleodemography. In: In: Hoppa RD , Vaupel JW , editors. Paleodemography. Age distributions from skeletal samples. Cambridge studies in biological and evolutionary anthropology 31. Cambridge: Cambridge University Press. pp129-168.

- Xu XH, Philipsen HP, Jablonski NG, Weatherhead B, Pang KM, Zhu JZ (1991) Preliminary report on a new method of human age estimation from single adult teeth. Forensic Science International, 51:281-288.

- Zhang Z (1982) A preliminary study of estimation of age by morphological changes in the symphysis pubis. Acta Anthropologica Sinica, 1:132-136.

- Zuhrt R (1955) Stomatologische Untersuchungen an

Spätmittelalterlichen Funden von Reckkahn (12-14 Jh.): I. Die Zahnkaries und ihre Folgen. Deutsche Zahn- Mund und Kieferheilkunde, 25:1-15.

Abbildung in dieser Leseprobe nicht enthalten

Abbildung in dieser Leseprobe nicht enthalten

Abbildung in dieser Leseprobe nicht enthalten

[...]


[1] Image adopted from Wegener R. et al. Zur Schätzung des Alters anhand der Zahnwurzeltransparenz (Estimation of Age from Root Dentine Transparency ) Z Rechtsmed 86, 29-34 ,1980,. Artictle in german with English abstract

[1] The Anderson-Darling test is used to test if a sample of data comes from a specific distribution. In its application as a test that the normal distribution adequately describes a set of data, it is one of the most powerfulstatistics fordetecting most departuresfrom normality

[2] QQplot (X) displays a quantile-quantile plot of the sample quantiles of X versus theoretical quantiles from a normal distribution. Ifthe distribution ofX is normal, the plot will be close to linear

[3] Spearman's rank correlation coefficient or Spearman's rho, named after Charles Spearman and often denoted by the Greek letter p (rho) or as rs, is a non-parametric measure of correlation - that is, it assesses how well an arbitrary monotonic function could describe the relationship between two variables, without making any assumptions about the frequency distribution ofthe variable.

[4] The Kendall tau rank correlation coefficient (or simply the Kendall tau coefficient, Kendall's τ or tau test(s)) is a non-parametric statistic used to measure the degree of correspondence between two rankings and assessing the significance of this correspondence. In other words, it measures the strength ofassociation.

[5] Residuals are the vertical distances of each point from the regression line A residual plot is a graph that shows the residuals on the vertical axis and the independent variable on the horizontal axis. If the points in a residual plot are randomly dispersed around the horizontal axis, a linear regression model is appropriate for the data; otherwise, a non-linear model is more appropriate

[6] Saglam Atsu S, Sema Aka P, Ibrahim N. Dęlerin Kok §effafhgndan Ya§ Tespiti (Age estimation from root transparency). Turkiye KlinikleriJ DentalSci 2006,12 :46-52.( Paperin Turkish with English abstract)

[7] Cronbach's a (alpha) has an important use as a measure of the reliability of the test items and the mean inter­correlation among the items it will generally increase when the correlations between the items increase.

[8] The bootstrap bias corrected accelerated (BCa) interval is amodification of the percentile method that adjusts the percentiles to correct for bias and skewness.

[9] The Mann-Whitney U test (also called the Mann-Whitney-Wilcoxon (MWW), Wilcoxon rank-sum test, or Wilcoxon-Mann-Whitney test) is a non-parametric test for assessing whether two independent samples of observations come from the same distribution. It is one of the best-known non-parametric significance tests. It was proposed initially by Wilcoxon (1945), for equal sample sizes, and extended to arbitrary sample sizes and in other ways by Mann and Whitney (1947). MWW is virtually identical to performing an ordinary parametric two- sample t test on the data after ranking over the combined samples.

Details

Pages
148
Year
2010
ISBN (Book)
9783656326793
File size
35.4 MB
Language
English
Catalog Number
v202493
Institution / College
Cairo University – Institute of African Research and Studies
Grade
Tags
comparative study methods determination using nubian dental sample

Share

Previous

Title: A Comparative Study of Two Methods for Age Determination Using Nubian Dental Sample