Gender Estimation with Parameters Obtained From the Upper Dental Arcade by Using Machine Learning Algorithms and Artificial Neural Networks

Basit Öğe Kaydı
dc.authoridErkartal, Halil Şaban/0000-0002-6558-3265
dc.authoridDUMAN, SUAYIP BURAK/0000-0003-2552-0187
dc.authoridSECGIN, YUSUF/0000-0002-0118-6711
dc.authorwosidErkartal, Halil Şaban/HHS-2544-2022
dc.contributor.authorErkartal, Halil Saban
dc.contributor.authorTatli, Melike
dc.contributor.authorSecgin, Yusuf
dc.contributor.authorToy, Seyma
dc.contributor.authorDuman, Suayip Burak
dc.date.accessioned2024-08-04T20:11:43Z
dc.date.available2024-08-04T20:11:43Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractObjective: The aim of this study is to predict gender with parameters obtained from the upper dental arch by using machine learning algorithms (ML) machine learning algorithms and artificial neural networks to provide optimum aesthetics, functionality, long-term stability, diagnosis and treatment intervention in orthodontics, forensic medicine and anthropology. Methods: The study was conducted on cone-beam computed tomography (CBCT) images of 176 individuals between the ages of 18 and 55 who did not have any pathologies or surgical interventions in their upper dental arcade. The images obtained were transferred to RadiAnt DICOM Viewer program in Digital Imaging and Communications in Medicine format and all images were brought to orthogonal plane by applying 3D Curved Multiplanar Reconstruction. Length and curvature length measurements were performed on these images brought to orthogonal plane. The data obtained were used in ML algorithms and artificial neural networks input and rates of gender estimation were shown. Results: In the study, an accuracy ratio of 0.86 was found with ML models linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression (LR) algorithm and an accuracy ratio of 0.86 was found with random forest (RF) algorithm. It was found with SHAP analyser of RF algorithm that the parameter of width at the level of 3rd molar teeth contributed the most to gender. An accuracy rate of 0.92 was found as a result of training for 500 times with multi-layer classifier perceptron (MLCP), which is an artificial neural network (ANN) model. Conclusion: As a result of our study, it was found that the parameters obtained from the upper dental arcade showed a high accuracy in estimation of gender. In this context, we believe that the present study will make important contributions to forensic sciences.en_US
dc.identifier.doi10.58600/eurjther1606
dc.identifier.endpage358en_US
dc.identifier.issn2564-7784
dc.identifier.issn2564-7040
dc.identifier.issue3en_US
dc.identifier.startpage352en_US
dc.identifier.trdizinid1199952en_US
dc.identifier.urihttps://doi.org/10.58600/eurjther1606
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1199952
dc.identifier.urihttps://hdl.handle.net/11616/92944
dc.identifier.volume29en_US
dc.identifier.wosWOS:001158410800018en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherPera Yayincilik Hizmetlerien_US
dc.relation.ispartofEuropean Journal of Therapeuticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectUpper dental arcadeen_US
dc.subjectcone-beam computed tomographyen_US
dc.subjectestimation of genderen_US
dc.subjectmachine learning algorithmsen_US
dc.subjectartificial neural networksen_US
dc.titleGender Estimation with Parameters Obtained From the Upper Dental Arcade by Using Machine Learning Algorithms and Artificial Neural Networksen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
TR-Dizin İndeksli Yayınlar Koleksiyonu