Machine learning and regression analysis for age estimation from the iliac crest based on computed tomographic explorations in an Indian population

Journal article


Warrier, V., Shedge, R., Garg, P.K., Dixit, S.G., Krishan, K. and Kanchan, T. 2023. Machine learning and regression analysis for age estimation from the iliac crest based on computed tomographic explorations in an Indian population. Medicine, Science, and the Law. 64 (3). https://doi.org/10.1177/00258024231198917
AuthorsWarrier, V., Shedge, R., Garg, P.K., Dixit, S.G., Krishan, K. and Kanchan, T.
Abstract

Age estimation constitutes an integral parameter of identification. In children, sub-adults, and young adults, accurate age estimation is vital on various aspects of civil, criminal, and immigration law. The iliac crest presents as a suitable age marker within these age cohorts, and the modified Risser method constitutes a relatively novel and unexplored method for iliac crest age estimation. The present study attempted to ascertain the applicability of this modified method for age estimation in the Indian population, an aspect previously unexplored, through computed tomographic examination of the iliac crest. Computed tomography scans of consenting individuals undergoing routine examinations of the pelvis/ abdomen for various clinically indicated reasons were collected and scored using the modified Risser stages. Computed tomographic examinations of the iliac crest indicate that the recalibrated method accurately depicts the temporal progression of ossification and fusion changes. Different regression and machine learning models were subsequently derived and/or trained to evaluate the accuracy and precision associated with the method. Amongst the ten regression models derived herein, compound regression exhibited the lowest inaccuracy (4.78 years) and root mean squared error values (5.46 years). Machine learning yielded further reduced error rates, with decision tree regression achieving inaccuracy and root mean squared error values of 1.88 years and 2.28 years, respectively. A comparative evaluation of error computations obtained from regression analysis and machine learning illustrates the statistical superiority of machine learning for forensic age estimation. Error computations obtained with machine learning suggest that the modified Risser method is capable of permitting reliable age estimation within criminal and civil proceedings.

KeywordsAge estimation; Computed tomography; Iliac crest; Machine Learning; Modified Risser stages; Regression models
Year2023
JournalMedicine, Science, and the Law
Journal citation64 (3)
PublisherSAGE Publications
ISSN 2042-1818
Digital Object Identifier (DOI)https://doi.org/10.1177/00258024231198917
Web address (URL)https://journals.sagepub.com/doi/10.1177/00258024231198917
https://pubmed.ncbi.nlm.nih.gov/37670580/
Output statusPublished
Publication dates
Online05 Sep 2023
Publication process dates
Deposited11 Jul 2024
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