Development of a biometric method to estimate age on hand radiographs

Age estimation from mandibles in Malay: A 2D geometric morphometric analysis

Objectives

In this study, the sizes and forms of mandibles in various age groups of the Malay population were measured and compared.

Methods

Geometric morphometric (GM) analysis of mandibles from 400 dental panoramic tomography (DPT) specimens was conducted. The MorphoJ program was used to perform generalized Procrustes analysis (GPA), Procrustes ANOVA, principal component analysis (PCA), discriminant function analysis (DFA), and canonical variate analysis (CVA). In the tpsDig2 program, the 27 landmarks were applied to the DPT radiographs. Variations in mandibular size and form were categorized into four age groups: group 1 (15-24 years), group 2 (25-34 years), group 3 (35-44 years), and group 4 (45-54 years).

Results

The diversity in mandibular shape among the first eight principal components was 81%. Procrustes ANOVA revealed significant shape differences (P < 0.001) among age groups. Mahalanobis distances indicated substantial differences among all age groups; group 1 and group 4 scored highest, at 2.114. The ranges for the cross-validation and discriminant function tests were 90-72% and 81-49%, respectively.

Conclusion

GM analysis through radiography is a simple, non-invasive, and non-destructive method of estimating age by using the mandible. GM analysis is unique because it can visualize the changes in mandible shape among age groups. This method should aid in age identification in forensic odontology investigations.

Should we consider the sex when estimating bone age from hand bone biometrics?

BACKGROUND

Recently, it was proposed to estimate age from the biometric information of hand bones. We observed that these estimations became less precise as children get older, especially from the age of 13-15 years.

OBJECTIVE

This study aimed to evaluate the influence of considering sex for age estimation based on hand bones biometrics.

MATERIALS AND METHODS

The study sample consisted of metacarpals and proximal phalanges measurements collected on 1003 medical images performed at Nancy and Marseille Hospitals of individuals aged under 21 years. This sample was divided into two subgroups delineated by the age of 13, as it is a relevant legal threshold for most European countries. First, the influence of sex on the hand bones biometrics and on the estimated age was evaluated. Then, based on these results, new sex-specific age estimation formulas were constructed from linear models and their precision was assessed using residual analysis, in comparison with previous global formulas.

RESULTS

An influence of sex was only highlighted from the age of 13 and for the total study sample. Thus, new sex-specific age estimation formulas were built for the [1-21] global sample and the [13-21] subsample. Even though the differences with the previous formulas were minor, age was more accurately estimated when sex was considered.

CONCLUSION

Considering sex in age estimation is relevant when relying on hand bone biometrics. A new tool was proposed to select the most appropriate age estimation formula, based on the discriminant analysis result and the a priori knowledge of the sex.

Age estimation from the biometric information of hand bones: Development of new formulas

INTRODUCTION

In the judicial context of the age estimation of living individuals, a new method was recently proposed, based on the collection of biometric information on hand bones radiographs. The aim of this study was to apply this method to a large French sample to provide new tools for age estimation MATERIALS AND METHODS: The study sample consisted of metacarpals and proximal phalanges measurements of 1003 individuals aged less than 21 years. This sample was divided into two subgroups 1-12 and 13-21 years as the age of 13 is a relevant legal threshold for most European countries. A quadratic discriminant analysis was performed to identify the group to which an individual was most likely to belong. Age estimation formulas were also constructed from linear models: for each subgroup and the total sample.

RESULTS

The belonging of an individual to the 1-12 or 13-21 subgroup was determined with a correct classification rate of 89.8%. Age estimation formulas became less precise with age, with a mean absolute error ranging between 11 and 21 months.

CONCLUSION

We proposed a two-step procedure for age estimation: firstly, the identification of the age group to which the individual is most likely to belong, and secondly, the age estimation of this individual by applying the appropriate formula.