Machine Learning-Based Sex Classification Using Linear Dimensions of the Talocrural Joint

Background: Pelvic and cranial markers are often fragmented in mass disasters. The resilient talocrural joint serves as a valuable secondary site. This study presents a novel 12-landmark configuration on conventional 2D AP ankle radiographs as a rapid, cost-effective auxiliary sex estimation tool, bypassing complex 3D reconstructions. Methods: Radiographs of 200 contemporary Turkish adults (100 males, 100 females) were calibrated via PACS DICOM metadata. Twelve landmarks mapping distal tibial (T1–T6) and fibular (F7–F12) cortical topography were tracked to derive linear metrics. Models were evaluated using 5-fold cross-validation. Results: Optimized logistic regression achieved the highest independent test accuracy of 80.0%, outperforming random forests (72.5%) and support vector machines (70.0%), with an AUC of 0.870 (95% CI: 0.765–0.975). Tibial T2–T3 and T6–T1 were the strongest predictors (p < 0.001, Cohen’s d > 1.5). Although fibular F9–F10 lost significance after Bonferroni correction (p = 0.552), it maintained substantial multivariate weight, indicating spatial synergy. Conclusions: This interpretable, regularized logistic regression model trained on minimal linear ankle metrics shows that biological sex can be estimated with promising accuracy (80.0%) as an auxiliary tool capable of being seamlessly integrated into clinical PACS and active forensic workflows.