Machine learning model for sex determination in adults based on two-dimensional cephalometric measurements of the mandibular symphysis

This study aimed to develop machine learning-based predictive models for sex determination using the height and width of the mandibular symphysis from two-dimensional lateral cephalometric images. Data from 495 adult patients who underwent lateral cephalometric radiography at a private clinic in southern Brazil were analyzed. Mandibular symphysis measurements were collected and used to train eight supervised machine-learning algorithms. The models were optimized using Grid Search and evaluated through a 5-fold cross-validation. Metrics such as Area Under the Curve, precision, sensitivity, and F1-score were calculated, with confidence intervals estimated via bootstrapping. The analyses showed that both the height and width of the mandibular symphysis exhibited statistically significant differences between the sexes (p < 0.05). The models achieved Area Under the Curve values ranging from 0.77 [95% CI: 0.69–0.85] to 0.60 [95% CI: 0.46–0.62] in testing, and from 0.78 [95% CI: 0.76–0.82] to 0.68 [95% CI: 0.64–0.74] in the cross-validation. Among the evaluated algorithms, the SVM, Logistic Regression, and K-Nearest Neighbors models demonstrated the highest predictive performance. Machine learning enabled sex determination based on mandibular symphysis measurements. The results suggest that this approach may serve as a complementary tool in forensic anthropology. Future studies should validate these models in different populations to ensure their generalizability. These findings suggest that the application of artificial intelligence methods can enhance the accuracy of sex determination, contributing to significant advancements in the field.