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Forensic Age Estimation: A Multifactorial Approach in a Retrospective Population Study

Authors :
Monika Bjelopavlovic
Sebastian R. Reder
Isabel Fritzen
Marc A. Brockmann
Jochen Hardt
Katja Petrowski
Source :
Diagnostics, Vol 13, Iss 12, p 2029 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

Objectives: The objective of this study was to evaluate the accuracy of forensic age estimation in a German population by combining clavicle and wisdom teeth assessments based on cone beam computed tomography (CT) data. The study aimed to determine the reliability of this approach in predicting biological age. Material and Methods: A total of 161 CT data sets from 120 males and 41 females with known exact ages were evaluated by three raters. The clavicle was assessed according to stages 1–5 (including substages 2a–c and 3a–c), and the Demirjian stage’s classification method was used for the wisdom teeth. Inter-class correlation (ICC) was calculated to assess the agreement among the three raters. Additionally, ordinary least square regressions were performed to predict chronological age using the clavicle or one of the four teeth. Finally, age prediction models using multiple indicators were developed. Results: The ICCs ranged from 0.82 for the clavicle to 0.86 and 0.88 for the wisdom teeth. Linear estimation tended to overestimate chronological age, especially in subjects over 18 years old. The clavicle showed the strongest overestimation. Combining age estimation from the clavicle with the upper and lower wisdom teeth improved the predictive power, resulting in a 14% and 15% increase in R² for the upper and lower wisdom teeth, respectively. Adding more than one tooth to the prediction did not improve the predictive power (all ΔR² < 1%). Conclusions: Age estimation using CT can be significantly improved by combining information from the analysis of wisdom teeth with age estimation based on the clavicle.

Details

Language :
English
ISSN :
13122029 and 20754418
Volume :
13
Issue :
12
Database :
Directory of Open Access Journals
Journal :
Diagnostics
Publication Type :
Academic Journal
Accession number :
edsdoj.1c757a44ec0045caa7022d2e09c4e02a
Document Type :
article
Full Text :
https://doi.org/10.3390/diagnostics13122029