Sex and age estimation with machine learning algorithms with parameters obtained from cone beam computed tomography images of maxillary first molar and canine teeth

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dc.authoridSECGIN, YUSUF/0000-0002-0118-6711en_US
dc.authorscopusid57190176466en_US
dc.authorscopusid57208820456en_US
dc.authorscopusid58300089700en_US
dc.authorscopusid57222366368en_US
dc.authorscopusid56472611800en_US
dc.contributor.authorSenol, Deniz
dc.contributor.authorSecgin, Yusuf
dc.contributor.authorDuman, Burak Suayip
dc.contributor.authorToy, Seyma
dc.contributor.authorOner, Zulal
dc.date.accessioned2024-08-23T16:04:04Z
dc.date.available2024-08-23T16:04:04Z
dc.date.issued2023en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractBackgroundThe aim of this study is to obtain a highly accurate and objective sex and age estimation by using the parameters of maxillary molar and canine teeth obtained from cone beam computed tomography images in the input of machine learning algorithms. Cone beam computed tomography images of 240 people aged between 25 and 54 were randomly selected from the archive systems of the hospital and transferred to Horos Medikal. 3D curved multiplanar reconstruction was applied to these images and a 3D image was obtained. The resulting image was brought to the orthogonal plane and the measurements were made by superimposing them.ResultsThe results were grouped in four different age groups (25-30, 31-36, 37-49, 50-54) and recorded. As a result of our study, the highest accuracy rate was found as 0.81 in sex estimation with ADA Boost Classifier algorithm, while in age estimation, the highest accuracy rate was found as 0.84 between 25-30 and 31-36 age groups with random forest algorithm, as 0.74 between 25-30 and 37-49 age groups with random forest and ADA Boost Classifier algorithms and as 0.85 between 25-30 and 50-54 age groups with random forest algorithm.ConclusionsOur study differs from other studies in two aspects; the first is the selection of a sensitive method such as cone beam computed tomography, and the second is the selection of machine learning algorithms. As a result of our study, the highest accuracy rate was found as 0.81 in sex estimation and as 0.85 in age estimation with parameters of maxillary canine and molar teeth.en_US
dc.identifier.doi10.1186/s41935-023-00346-1
dc.identifier.issn2090-536X
dc.identifier.issn2090-5939
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85160949515en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1186/s41935-023-00346-1
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14051
dc.identifier.volume13en_US
dc.identifier.wosWOS:000998582000001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInt Assoc Law & Forensic Sciencesen_US
dc.relation.ispartofEgyptian Journal of Forensic Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSex estimationen_US
dc.subjectAge estimationen_US
dc.subjectCone beam computed tomographyen_US
dc.subjectMachine learning algorithmsen_US
dc.subjectMaxillary canine and molar teethen_US
dc.titleSex and age estimation with machine learning algorithms with parameters obtained from cone beam computed tomography images of maxillary first molar and canine teethen_US
dc.typeArticleen_US

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