Sex Estimation Based on Optical Channel Parameters from Computed Tomography Images with Machine Learning Algorithms

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dc.contributor.authorOzturk, Oguzhan
dc.contributor.authorHarmandaoglu, Oguzhan
dc.contributor.authorKaya, Seren
dc.contributor.authorSecgin, Yusuf
dc.contributor.authorSenol, Deniz
dc.contributor.authorColakoglu, Serdar
dc.contributor.authorOnbas, Omer
dc.date.accessioned2026-07-01T11:40:18Z
dc.date.available2026-07-01T11:40:18Z
dc.date.issued2025
dc.departmentDüzce Üniversitesi
dc.description.abstractThe 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.
dc.identifier.endpage2162
dc.identifier.issn0717-9502
dc.identifier.issn0717-9367
dc.identifier.issue6
dc.identifier.scopus2-s2.0-105035965685
dc.identifier.scopusqualityN/A
dc.identifier.startpage2155
dc.identifier.urihttps://hdl.handle.net/20.500.12684/23749
dc.identifier.volume43
dc.identifier.wosWOS:001698176100006
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSoc Chilena Anatomia
dc.relation.ispartofInternational Journal of Morphology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260623
dc.subjectSex Estimation
dc.subjectMachine Learning Algorithms
dc.subjectSphenoid Bone
dc.subjectOptic Canal
dc.titleSex Estimation Based on Optical Channel Parameters from Computed Tomography Images with Machine Learning Algorithms
dc.typeArticle

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