Gender estimation from morphometric measurements of mandibular lingula by using machine learning algorithms and artificial neural networks

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dc.authoridSECGIN, YUSUF/0000-0002-0118-6711
dc.authoridDUMAN, SUAYIP BURAK/0000-0003-2552-0187
dc.contributor.authorSenol, D.
dc.contributor.authorBodur, F.
dc.contributor.authorSecgin, Y.
dc.contributor.authorSencan, D.
dc.contributor.authorDuman, Sb
dc.contributor.authorÖner, Zülal
dc.date.accessioned2025-03-20T09:50:29Z
dc.date.available2025-03-20T09:50:29Z
dc.date.issued2024
dc.departmentİzmir Bakırçay Üniversitesi
dc.description.abstractBackground:Sex determination from the bones is of great importance for forensic medicine and anthropology. The mandible is highly valued because it is the strongest, largest and most dimorphic bone in the skull.Aim:Our aim in this study is gender estimation with morphometric measurements taken from mandibular lingula, an important structure on the mandible, by using machine learning algorithms and artificial neural networks.Methods:Cone beam computed tomography images of the mandibular lingula were obtained by retrospective scanning from the Picture Archiving Communication Systems of the Department of Oral, Dental and Maxillofacial Radiology, Faculty of Dentistry, & Idot;n & ouml;n & uuml; University. Images scanned in Digital Imaging and Communications in Medicine (DICOM) format were transferred to RadiAnt DICOM Viewer (Version: 2020.2). The images were converted to 3-D format by using the 3D Volume Rendering console of the program. Eight anthropometric parameters were measured bilaterally from these 3-D images based on the mandibular lingula.Results:The results of the machine learning algorithms analyzed showed that the highest accuracy was 0.88 with Random Forest and Gaussian Naive Bayes algorithm. Accuracy rates of other parameters ranged between 0.78 and 0.88.Conclusions:As a result of the study, it is thought that mandibular lingula-centered morphometric measurements can be used for gender determination as well as bones such as the pelvis and skull as they were found to be highly accurate. This study also provides information on the anatomical position of the lingula according to gender in Turkish society. The results can be important for oral-dental surgeons, anthropologists, and forensic experts.
dc.identifier.doi10.4103/njcp.njcp_787_23
dc.identifier.endpage738
dc.identifier.issn1119-3077
dc.identifier.issn2229-7731
dc.identifier.issue6
dc.identifier.pmid38943297
dc.identifier.scopus2-s2.0-85197176598
dc.identifier.scopusqualityQ2
dc.identifier.startpage732
dc.identifier.urihttps://doi.org/10.4103/njcp.njcp_787_23
dc.identifier.urihttps://hdl.handle.net/20.500.14034/2215
dc.identifier.volume27
dc.identifier.wosWOS:001258543900011
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherWolters Kluwer Medknow Publications
dc.relation.ispartofNigerian Journal of Clinical Practice
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250319
dc.subjectArtificial neural network
dc.subjectgender estimation
dc.subjectmachine learning algorithms
dc.subjectmandibular lingula
dc.titleGender estimation from morphometric measurements of mandibular lingula by using machine learning algorithms and artificial neural networks
dc.typeArticle

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