Comparison of mandibular morphometric parameters in digital panoramic radiography in gender determination using machine learning

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dc.authoridHatipoglu, Omer/0000-0002-4628-8551
dc.contributor.authorPertek, Hanife
dc.contributor.authorKamasak, Mustafa
dc.contributor.authorKotan, Soner
dc.contributor.authorHatipoglu, Fatma Pertek
dc.contributor.authorHatipoglu, Omer
dc.contributor.authorKose, Taha Emre
dc.date.accessioned2024-11-07T13:35:01Z
dc.date.available2024-11-07T13:35:01Z
dc.date.issued2024
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractObjectiveThis study aimed to evaluate the usability of morphometric features obtained from mandibular panoramic radiographs in gender determination using machine learning algorithms.Materials and methodsHigh-resolution radiographs of 200 patients aged 20-77 (41.0 +/- 12.7) were included in the study. Twelve different morphometric measurements were extracted from each digital panoramic radiography included in the study. These measurements were used as features in the machine learning phase in which six different machine learning algorithms were used (k-nearest neighbor, decision trees, support vector machines, naive Bayes, linear discrimination analysis, and neural networks). To evaluate the reliability, we have performed tenfold cross-validation and we repeated this 10 times for every classification process. This process enhances the reliability of the results for other datasets.ResultsWhen all 12 features are used together, the accuracy rate is found to be 82.6 +/- 0.5%. The classification accuracies are also compared using each feature alone. Three features that give the highest accuracy are coronoid height (80.9 +/- 0.9%), condyle height (78.2 +/- 0.5%), and ramus height (77.2 +/- 0.4%), respectively. When compared to the classification algorithms, the highest accuracy was obtained with the naive Bayes algorithm with a rate of 84.0 +/- 0.4%.ConclusionMachine learning techniques can accurately determine gender by analyzing mandibular morphometric structures from digital panoramic radiographs. The most precise results are achieved by evaluating the structures in combination, using attributes obtained from applying the MRMR algorithm to all features.
dc.description.sponsorshipThe present article has been sourced from Hanife Pertek's Master's dissertation.
dc.identifier.doi10.1007/s11282-024-00751-9
dc.identifier.endpage423
dc.identifier.issn0911-6028
dc.identifier.issn1613-9674
dc.identifier.issue3
dc.identifier.pmid38625432
dc.identifier.scopus2-s2.0-85190524847
dc.identifier.scopusqualityQ2
dc.identifier.startpage415
dc.identifier.urihttps://doi.org/10.1007/s11282-024-00751-9
dc.identifier.urihttps://hdl.handle.net/11480/16279
dc.identifier.volume40
dc.identifier.wosWOS:001249167000007
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofOral Radiology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectMachine learning
dc.subjectForensic science
dc.subject& Idot;mage processing
dc.subjectDigital panoramic radiography
dc.subjectGender determination
dc.subjectMandibular morphometric parameters
dc.titleComparison of mandibular morphometric parameters in digital panoramic radiography in gender determination using machine learning
dc.typeArticle

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