Estimativa de Idade por Tomografia

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Computed tomographic age estimation from the iliac 
crest and ischial tuberosity in an Indian population using 
supervised machine learning approaches
Varsha Warrier1, Rutwik Shedge2, Pawan Kumar Garg3, Shilpi Gupta Dixit4, 
Kewal Krishan5, Tanuj Kanchan1,*
1 Department of Forensic Medicine and Toxicology, All India Institute of Medical Sciences, Jodhpur, India, 342005
2 School of Forensic Sciences, National Forensic Sciences University, Tripura, India, 799001
3 Department of Diagnostic and Interventional Radiology, All India Institute of Medical Sciences, Jodhpur, India, 342005
4 Department of Anatomy, All India Institute of Medical Sciences, Jodhpur, India, 342005
5 Department of Anthropology (UGC Centre of Advanced Study), Panjab University, Chandigarh, India, 160014
* Corresponding author: tanujkanchan@yahoo.co.in; kanchant@aiimsjodhpur.edu.in
ORCID Varsha Warrier: https://orcid.org/0000-0002-4874-5090
ORCID Rutwik Shedge: https://orcid.org/0000-0002-4918-5769
ORCID Pawan Kumar Garg: https://orcid.org/0000-0002-5805-1869
ORCID Shilpi Gupta Dixit: https://orcid.org/0000-0002-4807-1663
ORCID Kewal Krishan: https://orcid.org/0000-0001-5321-0958
ORCID Tanuj Kanchan: https://orcid.org/0000-0003-0346-1075
With 2 figures and 4 tables
Abstract: Within the pelvis the iliac crest and ischial tuberosity display delayed ossification and fusion, thus, presenting as 
reliable maturity indicators. Amongst the different iliac crest and ischial tuberosity age estimation methods, the modified 
Kreitner-Kellinghaus stages constitute one of the more promising methods. The present study was directed towards estab-
lishing the applicability of the modified Kreitner-Kellinghaus method using five supervised machine learning approaches. 
Clinical CT scans of consenting individuals were collected and scored using the modified Kreitner-Kellinghaus method 
for the iliac crest and ischial tuberosity, independently. Age was subsequently estimated using different machine learning 
models. Cumulative scores computed from both markers were additionally employed for age estimation using machine 
learning. For iliac crest age estimation, Random Forest and Gradient Boosting Regression furnished lowest mean abso-
lute error (2.42 years) and root mean square error (3.06 years). For ischial tuberosity age estimation, Gradient Boosting 
Regression garnered the lowest computations of mean absolute error (2.60 years) and root mean square error (3.09 years). 
For cumulative score based age estimation, Support Vector Regression and Gradient Boosting Regression yielded lowest 
mean absolute error (2.48 years) and root mean square error (3.07 years). Obtained error computations indicate that the 
iliac crest is a more accurate age marker in comparison to the ischial tuberosity. Additionally, cumulative score-based 
approaches garnered similar/ marginally more precise results in comparison to the iliac crest with all five models. This mar-
ginal improvement is not sufficient to justify employing the relatively more complicated cumulative score-based approach 
for age estimation. Hence, whenever available, the iliac crest should be preferred over the ischial tuberosity/ cumulative 
score-based approaches for age estimation.
Keywords: forensic anthropology; human identification; age estimation; computed tomography; iliac crest; ischial tuber-
osity; modified Kreitner-Kellinghaus stages; machine learning
Introduction
Age estimation occupies a prominent niche within the 
human identification process (Krogman & Iscan 1986). 
Accurately differentiating between a child, sub-adult, and 
young adult is crucial under various scenarios pertaining 
to civil, criminal, and immigration law (Wittschieber et al. 
2013a; Wittschieber et al. 2013b; Albayrak 2017). Over the 
years numerous methods have been devised to enable accu-
rate age estimation within these age cohorts. Most of these 
Anthropol. Anz. 81/3 (2024), 301–314 Article
J. Biol. Clin. Anthropol.
Published online 20 October 2023, published in print June 2024 
© 2023 E. Schweizerbart’sche Verlagsbuchhandlung, 70176 Stuttgart, Germany www.schweizerbart.de
DOI: 10.1127/anthranz/2023/1723 0003-5548/2023/1723 $ 3.50
mailto:tanujkanchan@yahoo.co.in
mailto:kanchant@aiimsjodhpur.edu.in
https://orcid.org/0000-0002-4874-5090
https://orcid.org/0000-0002-4918-5769
https://orcid.org/0000-0002-5805-1869
https://orcid.org/0000-0002-4807-1663
https://orcid.org/0000-0001-5321-0958
https://orcid.org/0000-0003-0346-1075
https://www.schweizerbart.de