Evaluating the accuracy of sex estimation from human tooth volume: leveraging automated AI segmentation and comparative analysis of machine learning algorithms.
Authors
Affiliations (9)
Affiliations (9)
- Postdoctoral Mobile Station of Basic Medical Science, Hebei Medical University, Shijiazhuang, China.
- College of Forensic Medicine, Hebei Medical University, Shijiazhuang, China.
- College of Forensic Medicine, Hebei Medical University, Shijiazhuang, China. [email protected].
- Department of Oral Implantology, Hebei Key Laboratory of Stomatology, Hebei Clinical Research Center for Oral Diseases, Hebei Technology Innovation Center of Oral Health, School and Hospital of Stomatology, Hebei Medical University, Shijiazhuang, China. [email protected].
- School of Stomatology, Hebei Medical University, Shijiazhuang, China.
- Department of Oral Implantology, Hebei Key Laboratory of Stomatology, Hebei Clinical Research Center for Oral Diseases, Hebei Technology Innovation Center of Oral Health, School and Hospital of Stomatology, Hebei Medical University, Shijiazhuang, China.
- Hebei Key Laboratory of Forensic Medicine, Hebei Collaborative Innovation Center of Forensic Medical Molecular Identification, Shijiazhuang, China.
- College of Forensic Medicine, Hebei Medical University, Shijiazhuang, China. [email protected].
- Hebei Key Laboratory of Forensic Medicine, Hebei Collaborative Innovation Center of Forensic Medical Molecular Identification, Shijiazhuang, China. [email protected].
Abstract
Accurately estimating biological sex is a fundamental step in forensic odontology, anthropology and archaeology. Tooth volume has been recognized as a valuable feature for sex estimation. However, dental morphology exhibits population-specific variations, which remains underexplored in the northern Han Chinese population. Furthermore, manual segmentation of cone-beam computed tomography (CBCT) images is prone to subjectivity and error, and conventional modeling approaches may offer limited predictive accuracy. This study aimed to address these limitations by integrating automated artificial intelligence (AI) segmentation with multiple machine learning algorithms to enhance objectivity and predictive performance in sex prediction. A cohort of 398 CBCT images was analyzed using a fully automated deep learning system for tooth segmentation, and a final cohort of 357 samples (185 females, 172 males) was retained. Volumetric data for teeth were automatically calculated, with missing values imputed via multiple imputation by chained equations (MICE). Four machine learning algorithms were applied to build binary classification models for sex estimation. Hyperparameter optimization was achieved through nested cross-validation, and model performance was evaluated on a test set using a comprehensive set of metrics. All 16 measured teeth showed significantly differences in volume between sexes, with males consistently showing larger mean volumes across all tooth types compared to females. Nested cross-validation showed that all models exhibited promising performance for sex estimation with mean ACC above 0.77 and mean AUC values at or above 0.85. On the test set, the models achieved accuracies ranging from 0.803 to 0.859 and AUCs from 0.893 to 0.903. SHAP interpretability analysis highlighted canines as having the greatest impact on predictions. This study is the first to demonstrate the significant sexual dimorphism of tooth volumes in a north Chinese population. We utilized an AI-based automated segmentation accompanied with machine learning modeling pipeline to accurately estimate sex using tooth volume features, offering a valuable approach for forensic and anthropological applications.