Abstract
This study investigates a deep learning approach for sex estimation using 3D hyoid bone models derived from computed tomography (CT) scans of a Croatian population. We analyzed 202 hyoid samples (101 male, 101 female), converting CT-derived meshes into 2048-point clouds for processing with an adapted PointNet++ network. The model, optimized for small datasets with 1D convolutional layers and global size features, was first applied in an unsupervised framework. Unsupervised clustering achieved 87.10% accuracy, identifying natural sex-based morphological patterns. Subsequently, supervised classification with a support vector machine yielded an accuracy of 88.71% (Matthews Correlation Coefficient, MCC = 0.7746) on a test set (n = 62). Interpretability analysis highlighted key regions influencing classification, with males exhibiting larger, U-shaped hyoids and females showing smaller, more open structures. Despite the modest sample size, the method effectively captured sex differences, providing a data-efficient and interpretable tool. This flexible approach, combining computational efficiency with practical insights, demonstrates potential for aiding sex estimation in cases with limited skeletal remains and may support broader applications in forensic anthropology.