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Background <p>This study aimed to develop and evaluate supervised machine learning models for sex prediction using morphometric measurements of the mental foramen obtained from digital panoramic radiographs. A total of 535 individuals (45.5% male and 54.5% female) were included. Seven linear measurements related to the position of the mental foramen were collected and initially screened through univariate analysis. Variables showing statistically significant differences between sexes were used as input features to train several classification algorithms. Model optimization was performed using grid search combined with five-fold cross-validation, and performance was assessed using accuracy, precision, recall, F1-score, and Area Under the ROC Curve, with 95% confidence intervals estimated via bootstrapping.</p> Results <p>All mental foramen–related measurements showed statistically significant differences between sexes (p &lt; 0.001), with moderate to high effect sizes, particularly for distances relative to the inferior border of the mandible. Among the evaluated algorithms, the K-Nearest Neighbors and Support Vector Machine models achieved the best performance. On test data, the K-Nearest Neighbors model reached an accuracy of 0.75 (95% CI: 0.68–0.81), precision of 0.75 (95% CI: 0.68–0.81), and an AUC of 0.78 (95% CI: 0.70–0.85). Similarly, the Support Vector Machine achieved an accuracy of 0.71 (95% CI: 0.63–0.78), precision of 0.71 (95% CI: 0.63–0.78), and an AUC of 0.78 (95% CI: 0.71–0.86). Model performance remained consistent during cross-validation, suggesting stable performance across partitions.</p> Conclusion <p>The machine learning models developed were able to identify morphometric patterns of the mental foramen associated with sexual dimorphism, showing consistent performance across testing and cross-validation phases. These findings suggest that machine learning can be applied to sex prediction based on mental foramen measurements, serving as an auxiliary tool in forensic dentistry, particularly in scenarios where dental references are unavailable.</p>

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Use of the mental foramen as a marker for sex estimation using supervised machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月22日

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  • Ana Laura Borkovski,
  • Milena Sampaio Kuczera,
  • Ana Julia Borkovski,
  • Arthur Felipe Golin Silveira,
  • Isabela Bittencourt Basso,
  • Flares Baratto-Filho,
  • Erika Calvano Küchler,
  • Odilon Guariza-Filho,
  • Bianca Marques de Mattos de Araujo,
  • Angela Graciela Deliga Schroder,
  • Cristiano Miranda de Araujo

摘要

Background

This study aimed to develop and evaluate supervised machine learning models for sex prediction using morphometric measurements of the mental foramen obtained from digital panoramic radiographs. A total of 535 individuals (45.5% male and 54.5% female) were included. Seven linear measurements related to the position of the mental foramen were collected and initially screened through univariate analysis. Variables showing statistically significant differences between sexes were used as input features to train several classification algorithms. Model optimization was performed using grid search combined with five-fold cross-validation, and performance was assessed using accuracy, precision, recall, F1-score, and Area Under the ROC Curve, with 95% confidence intervals estimated via bootstrapping.

Results

All mental foramen–related measurements showed statistically significant differences between sexes (p < 0.001), with moderate to high effect sizes, particularly for distances relative to the inferior border of the mandible. Among the evaluated algorithms, the K-Nearest Neighbors and Support Vector Machine models achieved the best performance. On test data, the K-Nearest Neighbors model reached an accuracy of 0.75 (95% CI: 0.68–0.81), precision of 0.75 (95% CI: 0.68–0.81), and an AUC of 0.78 (95% CI: 0.70–0.85). Similarly, the Support Vector Machine achieved an accuracy of 0.71 (95% CI: 0.63–0.78), precision of 0.71 (95% CI: 0.63–0.78), and an AUC of 0.78 (95% CI: 0.71–0.86). Model performance remained consistent during cross-validation, suggesting stable performance across partitions.

Conclusion

The machine learning models developed were able to identify morphometric patterns of the mental foramen associated with sexual dimorphism, showing consistent performance across testing and cross-validation phases. These findings suggest that machine learning can be applied to sex prediction based on mental foramen measurements, serving as an auxiliary tool in forensic dentistry, particularly in scenarios where dental references are unavailable.