Machine Learning-Based Sex Classification Using Linear Dimensions of the Talocrural Joint
APPLIED SCIENCES-BASEL, vol.16, no.15, 2026 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 16 Issue: 15
- Publication Date: 2026
- Doi Number: 10.3390/app16157686
- Journal Name: APPLIED SCIENCES-BASEL
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, Compendex, INSPEC, Directory of Open Access Journals
- Istanbul University Affiliated: Yes
Abstract
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.