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

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%.