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This study aims to improve sex estimation accuracy using tooth volume analysis through automated AI segmentation and machine learning algorithms.

June 13, 2026Open Access

Evaluating the accuracy of sex estimation from human tooth volume: leveraging automated AI segmentation and comparative analysis of machine learning algorithms

Key Points

This study aims to improve sex estimation accuracy using tooth volume analysis through automated AI segmentation and machine learning algorithms.
Analyzed 398 CBCT images, retaining 357 samples (185 females, 172 males).
Employed automated deep learning for tooth segmentation and calculated volumetric data.
Applied four machine learning algorithms with hyperparameter optimization and evaluated performance using metrics.
Achieved mean accuracy above 0.77 and mean AUC values at or above 0.85 across models.
Test set accuracies ranged from 0.803 to 0.859, with AUCs from 0.893 to 0.903.
All measured teeth showed significant volume differences between sexes, with males having larger volumes.

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.

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