Evaluating the Effectiveness of Coxal Bone Measurements for Sex Estimation via Machine Learning

TL;DR

This study uses machine learning to accurately estimate sex from coxal bone measurements in human pelves.

Contribution

Applies machine learning to coxal bone data for sex estimation, achieving high classification accuracy.

Findings

Coxal bone dimensions show strong sexual dimorphism.

Machine learning models classify sex with 95-100% accuracy.

Some bilateral and age-related differences were also observed.

Abstract

Sex estimation plays a pivotal role in the reconstruction of the biological profile from skeletal remains across various branches of anthropological science. The human pelvis is a key structure in this process, as its morphology differs substantially between males and females due to the distinct demands of pregnancy and childbirth in females. Many studies have examined sex differences in the size and shape of the coxal bones, which form the major part of the pelvis; however, only a few have applied machine learning algorithms for this purpose. The present study applied such methods to evaluate the potential of coxal bone measurements and trained models to correctly classify male and female pelves.

The pelvis is the most dimorphic part of the human skeleton, primarily because of its involvement in the birth process. Many sexually dimorphic traits are concentrated in the coxal bones, which form the larger part of the birth canal. The present study aimed to assess the sex differences in coxal bone size and to develop machine learning (ML) models for sex estimation based on coxal bone measurements. The sample included abdominal computed tomography scans of 276 adult Bulgarians. Three-dimensional models of the pelves were generated using InVesalius. The three-dimensional coordinates of 34 landmarks located on the right and left coxal bones were collected in MeshLab. Based on the landmark coordinates, various measurements characterizing the coxal bones were calculated. The coxal bone dimensions were tested for significant differences with respect to sex, age, and laterality. Support Vector Machines and logistic regression were employed to train models for sex estimation. The results demonstrate strong sexual dimorphism in coxal bone dimensions along with some bilateral and age-related differences. The trained ML models classify male and female bones with very high accuracy, ranging between 95% and 100%.

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