Sex estimation from coxal bones using deep learning in a population balanced by sex and age

In the field of forensic anthropology, researchers aim to identify anonymous human remains and determine the cause and circumstances of death from skeletonized human remains. Sex determination is a fundamental step of this procedure because it influences the estimation of other traits, such as age a...

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Veröffentlicht in:International journal of legal medicine 2024-11, Vol.138 (6), p.2617-2623
Hauptverfasser: Epain, Marie, Valette, Sébastien, Zou, Kaifeng, Faisan, Sylvain, Heitz, Fabrice, Croisille, Pierre, Fracasso, Tony, Fanton, Laurent
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container_issue 6
container_start_page 2617
container_title International journal of legal medicine
container_volume 138
creator Epain, Marie
Valette, Sébastien
Zou, Kaifeng
Faisan, Sylvain
Heitz, Fabrice
Croisille, Pierre
Fracasso, Tony
Fanton, Laurent
description In the field of forensic anthropology, researchers aim to identify anonymous human remains and determine the cause and circumstances of death from skeletonized human remains. Sex determination is a fundamental step of this procedure because it influences the estimation of other traits, such as age and stature. Pelvic bones are especially dimorphic, and are thus the most useful bones for sex identification. Sex estimation methods are usually based on morphologic traits, measurements, or landmarks on the bones. However, these methods are time-consuming and can be subject to inter- or intra-observer bias. Sex determination can be done using dry bones or CT scans. Recently, artificial neural networks (ANN) have attracted attention in forensic anthropology. Here we tested a fully automated and data-driven machine learning method for sex estimation using CT-scan reconstructions of coxal bones. We studied 580 CT scans of living individuals. Sex was predicted by two networks trained on an independent sample: a disentangled variational auto-encoder (DVAE) alone, and the same DVAE associated with another classifier (C recon ). The DVAE alone exhibited an accuracy of 97.9%, and the DVAE + C recon showed an accuracy of 99.8%. Sensibility and precision were also high for both sexes. These results are better than those reported from previous studies. These data-driven algorithms are easy to implement, since the pre-processing step is also entirely automatic. Fully automated methods save time, as it only takes a few minutes to pre-process the images and predict sex, and does not require strong experience in forensic anthropology.
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subjects Algorithms
Anthropology
Artificial neural networks
Automation
Bioengineering
Bones
Chronology
Computed tomography
Computer Science
Deep learning
Forensic anthropology
Forensic computing
Forensic Medicine
Human remains
Imaging
Life Sciences
Machine Learning
Medical imaging
Medical Law
Medicine
Medicine & Public Health
Original Article
Sex
title Sex estimation from coxal bones using deep learning in a population balanced by sex and age
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