Sex estimation with parameters of the facial canal by computed tomography using machine learning algorithms and artificial neural networks

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dc.contributor.authorSecgin, Yusuf
dc.contributor.authorKaya, Seren
dc.contributor.authorHarmandaoglu, Oguzhan
dc.contributor.authorOzturk, Oguzhan
dc.contributor.authorSenol, Deniz
dc.contributor.authorOnbas, Omer
dc.contributor.authorYilmaz, Nihat
dc.date.accessioned2025-10-11T20:48:10Z
dc.date.available2025-10-11T20:48:10Z
dc.date.issued2025
dc.departmentDüzce Üniversitesien_US
dc.description.abstractBackgroundThe skull is highly durable and plays a significant role in sex determination as one of the most dimorphic bones. The facial canal (FC), a clinically significant canal within the temporal bone, houses the facial nerve. This study aims to estimate sex using morphometric measurements from the FC through machine learning (ML) and artificial neural networks (ANNs).Materials and methodsThe study utilized Computed Tomography (CT) images of 200 individuals (100 females, 100 males) aged 19-65 years. These images were retrospectively retrieved from the Picture Archiving and Communication Systems (PACS) at D & uuml;zce University Faculty of Medicine, Department of Radiology, covering 2021-2024. Bilateral measurements of nine temporal bone parameters were performed in axial, coronal, and sagittal planes. ML algorithms including Quadratic Discriminant Analysis (QDA), Linear Discriminant Analysis (LDA), Decision Tree (DT), Extra Tree Classifier (ETC), Random Forest (RF), Logistic Regression (LR), Gaussian Naive Bayes (GaussianNB), and k-Nearest Neighbors (k-NN) were used, alongside a multilayer perceptron classifier (MLPC) from ANN algorithms.ResultsExcept for QDA (Acc 0.93), all algorithms achieved an accuracy rate of 0.97. SHapley Additive exPlanations (SHAP) analysis revealed the five most impactful parameters: right SGAs, left SGAs, right TSWs, left TSWs and, the inner mouth width of the left FN, respectively.ConclusionsFN-centered morphometric measurements show high accuracy in sex determination and may aid in understanding FN positioning across sexes and populations. These findings may support rapid and reliable sex estimation in forensic investigations-especially in cases with fragmented craniofacial remains-and provide auxiliary diagnostic data for preoperative planning in otologic and skull base surgeries. They are thus relevant for surgeons, anthropologists, and forensic experts.Clinical trial numberNot applicable.en_US
dc.identifier.doi10.1186/s12880-025-01834-7
dc.identifier.issn1471-2342
dc.identifier.issue1en_US
dc.identifier.pmid40681971en_US
dc.identifier.scopus2-s2.0-105011083096en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1186/s12880-025-01834-7
dc.identifier.urihttps://hdl.handle.net/20.500.12684/21758
dc.identifier.volume25en_US
dc.identifier.wosWOS:001532005600001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherBmcen_US
dc.relation.ispartofBmc Medical Imagingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKA_WOS_20250911
dc.subjectSex estimationen_US
dc.subjectMachine learningen_US
dc.subjectArtificial neural networken_US
dc.subjectFacial canalen_US
dc.subjectFallopian canalen_US
dc.titleSex estimation with parameters of the facial canal by computed tomography using machine learning algorithms and artificial neural networksen_US
dc.typeArticleen_US

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