Gender Prediction Using Cone-Beam Computed Tomography Measurements from Foramen Incisivum: Application of Machine Learning Algorithms and Artificial Neural Networks

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dc.contributor.authorSenol, Deniz
dc.contributor.authorSecgin, Yusuf
dc.contributor.authorHarmandaoglu, Oguzhan
dc.contributor.authorKaya, Seren
dc.contributor.authorDuman, Suayip Burak
dc.contributor.authorOner, Zuelal
dc.date.accessioned2024-08-04T20:56:12Z
dc.date.available2024-08-04T20:56:12Z
dc.date.issued2024
dc.departmentİnönü Üniversitesien_US
dc.description.abstractIntroduction: This study aims to predict gender using parameters obtained from images of the foramen (for.) incisivum through cone-beam computed tomography (CBCT) and employing machine learning (ML) algorithms and artificial neural networks (ANN).Materials and Methods: This study was conducted on 162 individuals in total. Precise measurements were meticulously extracted, extending from the foramen incisivum to the arcus alveolaris maxillaris, through employment of CBCT. The ML and ANN models were meticulously devised, allocating 20% for rigorous testing and 80% for comprehensive training.Results: All parameters that are evaluated, except for the angle between foramen palatinum majus and foramen incisivum-spina nasalis posterior (GPFIFPNS-A), exhibited a significant gender difference. ANN and among the ML algorithms, logistic regression (LR), linear discriminant analysis (LDA), and random rorest (RF) demonstrated the highest accuracy (Acc) rate of 0.82. The Acc rates for other algorithms ranged from 0.76 to 0.79. In the models with the highest Acc rates, 14 out of 17 male individuals and 13 out of 16 female individuals in the test set were correctly predicted.Conclusion: LR, LDA, RF, and ANN yielded high gender prediction rates for the measured parameters, while decision tree, extra tree classifier, Gaussian Naive Bayes, quadratic discriminant analysis, and K-nearest neighbors algorithm methods provided lower predictions. We believe that the evaluation of measurements extending from foramen incisivum to arcus alveolaris maxillaris through CBCT scanning proves to be a valuable method in gender prediction.en_US
dc.identifier.doi10.4103/jasi.jasi_129_23
dc.identifier.endpage159en_US
dc.identifier.issn0003-2778
dc.identifier.issn2352-3050
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85197634487en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage152en_US
dc.identifier.urihttps://doi.org/10.4103/jasi.jasi_129_23
dc.identifier.urihttps://hdl.handle.net/11616/102097
dc.identifier.volume73en_US
dc.identifier.wosWOS:001265652200015en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWolters Kluwer Medknow Publicationsen_US
dc.relation.ispartofJournal of The Anatomical Society of Indiaen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial intelligenceen_US
dc.subjectforamen incisivumen_US
dc.subjectforensic anthropologyen_US
dc.subjectgender predictionen_US
dc.subjectmachine learningen_US
dc.subjectmaxillaen_US
dc.titleGender Prediction Using Cone-Beam Computed Tomography Measurements from Foramen Incisivum: Application of Machine Learning Algorithms and Artificial Neural Networksen_US
dc.typeArticleen_US

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