Ozturk, OguzhanHarmandaoglu, OguzhanKaya, SerenSecgin, YusufSenol, DenizColakoglu, SerdarOnbas, Omer2026-07-012026-07-0120250717-95020717-9367https://hdl.handle.net/20.500.12684/23749The skull is one of the most dimorphic and anatomically informative bones for sex estimation and shows resistance to taphonomic processes. This study aims to estimate sex using machine learning (ML) algorithms based on morphometric measurementof the optic canal (OC)-a clinically significant canal within the sphenoid bone that transmits the optic nerve and ophthalmicrtery. aThis retrospective study was conducted on CT from 260 adults (130 females and 130 males, aged 18-65). The images were obtained from the PACS archive of the Department of Radiology, Faculty of Medicine, D & uuml;zce University, covering the years 2019 to 2025.Sixteen bilateral morphometric parameters of the temporal bone were measured in axial and coronal planes. Data were analysed oususing vari ML algorithms, and classification performance was compared. On the 20 % test set, ML models achieved over 81 % accuracy;icLogist Regression performed best with 90 %. In 10-fold cross-validation, all algorithms exceeded 74 %, with LR again reaching theest highat 89 %. Decision Tree yielded the lowest accuracy. SHapley Additive exPlanations (SHAP), which facilitates interpretable machinelearning, revealed that the right-sided OC-midsagittal distance had the greatest predictive impact. Morphometric data from the OChighprovide accuracy and strong potential for sex estimation. The study also highlights sex-and population-based variation in OC Theseposition. findings may be relevant in clinical and forensic contexts, particularly in forensic anthropology, ophthalmology, and legalmedicine.eninfo:eu-repo/semantics/openAccessSex EstimationMachine Learning AlgorithmsSphenoid BoneOptic CanalArticle436215521622-s2.0-105035965685WOS:001698176100006N/AQ4