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Deep learning-based sex estimation of 3D hyoid bone models in a Croatian population using adapted PointNet++ network
Ivan Jerković1, Željana Bašić2, Ivana Kružić1
1Faculty of Forensic Sciences, University of Split, Split, Croatia.
Scientific Reports
|July 2, 2025
Summary
Deep learning accurately estimates sex using 3D hyoid bone models from CT scans. This method aids forensic anthropology by identifying sex-based morphological patterns in skeletal remains.
Area of Science:
- Forensic Anthropology
- Biomedical Engineering
- Computer Science
Background:
- Sex estimation from skeletal remains is crucial in forensic anthropology.
- Hyoid bone morphology presents potential for sex determination.
- Advanced computational methods can analyze complex 3D skeletal data.
Purpose of the Study:
- To develop and evaluate a deep learning approach for sex estimation using 3D hyoid bone models.
- To assess the accuracy and interpretability of the deep learning model.
- To explore the potential of this method in forensic anthropological applications.
Main Methods:
- Utilized 3D hyoid bone models derived from computed tomography (CT) scans of 202 individuals (101 male, 101 female) from a Croatian population.
- Converted CT-derived meshes into 2048-point clouds for processing with an adapted PointNet++ network.
- Employed unsupervised clustering and supervised classification (support vector machine) for sex estimation.
Main Results:
- Unsupervised clustering achieved 87.10% accuracy in identifying sex-based morphological patterns.
- Supervised classification yielded 88.71% accuracy (MCC = 0.7746) on a test set.
- Interpretability analysis revealed distinct morphological differences: males had larger, U-shaped hyoids; females had smaller, more open structures.
Conclusions:
- The deep learning approach effectively captures sex differences in hyoid bone morphology.
- The method is data-efficient and interpretable, showing potential for sex estimation with limited skeletal remains.
- This computational tool offers practical insights for forensic anthropology and related fields.
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