Sex estimation based on frontal sinus computed tomography images using machine learning and artificial neural networks

Basit Öğe Kaydı
dc.contributor.authorKaya, Seren
dc.contributor.authorHarmandaoglu, Oguzhan
dc.contributor.authorOzturk, Oguzhan
dc.contributor.authorSecgin, Yusuf
dc.contributor.authorSenol, Deniz
dc.contributor.authorOnbas, Omer
dc.date.accessioned2025-10-11T20:48:23Z
dc.date.available2025-10-11T20:48:23Z
dc.date.issued2025
dc.departmentDüzce Üniversitesien_US
dc.description.abstractDue to its anatomical uniqueness, the frontal sinus (FS) shows significant inter-individual differences by ancestry, age, and sex, making it useful for preliminary identification processes. This study aims to estimate sex using machine learning (ML) algorithms and artificial neural networks (ANN) applied to morphometric data from FS computed tomography (CT) images. This retrospective study analysed CT scans of 338 females and 338 males aged 18-65. FS measurements comprised sinus floor anteroposterior length, volume, area, height, depth, width, and anterior wall thickness (AWT). Sex estimation was performed using several ML algorithms, including Linear Discriminant Analysis, Quadratic Discriminant Analysis, Logistic Regression, Extra Trees Classifier, Decision Tree, Random Forest, k-Nearest Neighbours, and Gaussian Naive Bayes. Additionally, a multilayer perceptron classifier, representing ANN models, was utilized. The highest classification accuracy (94%) was achieved by the Logistic Regression model. According to the SHapley Additive exPlanations analysis, the two most influential parameters were identified as the right and left AWT, respectively. This study, with a comparatively large sample size, found that all morphometric FS parameters - especially AWT - hold significant potential in forensic identification. ML- and ANN-based models showed high classification accuracy, surpassing previous studies. These findings may guide future research involving diverse populations and regions.en_US
dc.identifier.doi10.1080/00450618.2025.2561625
dc.identifier.issn0045-0618
dc.identifier.issn1834-562X
dc.identifier.scopus2-s2.0-105016750528en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1080/00450618.2025.2561625
dc.identifier.urihttps://hdl.handle.net/20.500.12684/21890
dc.identifier.wosWOS:001572960400001en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Ltden_US
dc.relation.ispartofAustralian Journal of Forensic Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKA_WOS_20250911
dc.subjectFrontal sinusen_US
dc.subjectartificial neural networksen_US
dc.subjectmachine learningen_US
dc.subjectsex estimationen_US
dc.subjectparanasal sinusesen_US
dc.titleSex estimation based on frontal sinus computed tomography images using machine learning and artificial neural networksen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu