Sex estimation using foramen magnum measurements, discriminant analyses and artificial neural networks on an eastern Turkish population sample

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dc.authoridHEKIMOGLU, YAVUZ/0000-0001-9990-6045
dc.authoriddemir, uğur/0000-0003-3266-2861
dc.authoridKartal, Erhan/0000-0003-2459-7756
dc.authoridAsirdizer, Mahmut/0000-0001-7596-5892
dc.authorwosidHEKIMOGLU, YAVUZ/A-8409-2017
dc.authorwosiddemir, uğur/GQI-4632-2022
dc.authorwosidKartal, Erhan/AAX-4265-2020
dc.authorwosidEtli, Yasin/IAM-4569-2023
dc.authorwosidAsirdizer, Mahmut/AAA-2897-2020
dc.contributor.authorKartal, Erhan
dc.contributor.authorEtli, Yasin
dc.contributor.authorAsirdizer, Mahmut
dc.contributor.authorHekimoglu, Yavuz
dc.contributor.authorKeskin, Siddik
dc.contributor.authorDemir, Ugur
dc.contributor.authorYavuz, Alparslan
dc.date.accessioned2024-08-04T20:52:17Z
dc.date.available2024-08-04T20:52:17Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractBackground: Although many studies have been conducted using the foramen magnum for sex estimation, recent findings have indicated that the discriminant and regression models obtained from the foramen magnum may not be reliable. Artificial Neural Networks, was used as a classification technique in sex estimation studies on some other bones, did not used in sex estimation studies on the foramen magnum until now. The aim of this study was sex estimation on an Eastern Turkish population sample using foramen magnum measurements, discriminant analyses and Artificial Neural Networks. Methodology: The study was performed on the CT images of a total of 720 cases, comprising 360 males and 360 females. For sex estimation, discriminant analysis and Artificial Neural Networks were used. Results: The accuracy rate was 86.7% with discriminant analysis and when sex estimation accuracy was deter-mined according to cases with posterior probabilities above 95%, the accuracy ranged from 0% to 33.3%. With the use of the discriminant formulas of 2 other studies, obtained from different Turkish samples, sex could be determined at a rate of 84.6%. Some formulas were found to be unsuccessful in sex estimation. Sex estimation accuracy of 88.2% was achieved with Artificial Neural Networks.Conclusion: In this study, it was found that sex could be determined to some extent with discriminant formulas from other samples from the same population, although some formulas were unsuccessful. With the use of image processing techniques and machine learning algorithms, better results can be obtained in sex estimation.en_US
dc.identifier.doi10.1016/j.legalmed.2022.102143
dc.identifier.issn1344-6223
dc.identifier.pmid36084487en_US
dc.identifier.scopus2-s2.0-85137163376en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1016/j.legalmed.2022.102143
dc.identifier.urihttps://hdl.handle.net/11616/100868
dc.identifier.volume59en_US
dc.identifier.wosWOS:000861005300004en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherElsevier Ireland Ltden_US
dc.relation.ispartofLegal Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectForamen magnumen_US
dc.subjectSex estimationen_US
dc.subjectDiscriminant function analysisen_US
dc.subjectArtificial neural networksen_US
dc.subjectLinear discriminant function analysisen_US
dc.subjectStepwise discriminant analysisen_US
dc.titleSex estimation using foramen magnum measurements, discriminant analyses and artificial neural networks on an eastern Turkish population sampleen_US
dc.typeArticleen_US

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