Usage and comparison of artificial intelligence algorithms for determination of growth and development by cervical vertebrae stages in orthodontics

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dc.authorid0000-0002-5874-9474
dc.contributor.authorKök, Hatice
dc.contributor.authorAcılar, Ayşe Merve
dc.contributor.authorİzgi, Mehmet Said
dc.date.accessioned2020-03-26T20:19:42Z
dc.date.available2020-03-26T20:19:42Z
dc.date.issued2019
dc.departmentSelçuk Üniversitesi, Diş Hekimliği Fakültesi, Klinik Bilimler Bölümüen_US
dc.description.abstractBackground Growth and development can be determined by cervical vertebrae stages that were defined on the cephalometric radiograph. Artificial intelligence has the ability to perform a variety of activities, such as prediction-classification in many areas of life, by using different algorithms, In this study, we aimed to determine cervical vertebrae stages (CVS) for growth and development periods by the frequently used seven artificial intelligence classifiers, and to compare the performance of these algorithms with each other. Methods Cephalometric radiographs, that were obtained from 300 individuals aged between 8 and 17 years were included in our study. Nineteen reference points were defined on second, third, and 4th cervical vertebrae, and 20 different linear measurements were taken. Seven algorithms of artificial intelligence that are frequently used in the field of classification were selected and compared. These algorithms are k-nearest neighbors (k-NN), Naive Bayes (NB), decision tree (Tree), artificial neural networks (ANN), support vector machine (SVM), random forest (RF), and logistic regression (Log.Regr.) algorithms. Results According to confusion matrices decision tree, CSV1 (97.1%)-CSV2 (90.5%), SVM: CVS3 (73.2%)-CVS4 (58.5%), and kNN: CVS 5 (60.9%)-CVS 6 (78.7%) were the algorithms with the highest accuracy in determining cervical vertebrae stages. The ANN algorithm was observed to have the second-highest accuracy values (93%, 89.7%, 68.8%, 55.6%, and 78%, respectively) in determining all stages except CVS5 (47.4% third highest accuracy value). According to the average rank of the algorithms in predicting the CSV classes, ANN was the most stable algorithm with its 2.17 average rank. Conclusion In our experimental study, kNN and Log.Regr. algorithms had the lowest accuracy values. SVM-RF-Tree and NB algorithms had varying accuracy values. ANN could be the preferred method for determining CVS.en_US
dc.identifier.citationKök, H., Acilar, A. M., İzgi, M. S. (2019). Usage and Comparison of Artificial Intelligence Algorithms for Determination of Growth and Development by Cervical Vertebrae Stages in Orthodontics. Progress in Orthodontics, 20(1), 1-10.
dc.identifier.doi10.1186/s40510-019-0295-8en_US
dc.identifier.endpage10
dc.identifier.issn2196-1042en_US
dc.identifier.issue1en_US
dc.identifier.pmid31728776en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage1
dc.identifier.urihttps://dx.doi.org/10.1186/s40510-019-0295-8
dc.identifier.urihttps://hdl.handle.net/20.500.12395/38373
dc.identifier.volume20en_US
dc.identifier.wosWOS:000496707400001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorKök, Hatice.
dc.language.isoenen_US
dc.publisherSPRINGEROPENen_US
dc.relation.ispartofPROGRESS IN ORTHODONTICSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectArtificial intelligenceen_US
dc.subjectAlgorithmsen_US
dc.subjectCervical vertebraeen_US
dc.subjectGrowth and developmenten_US
dc.subjectOrthodonticsen_US
dc.titleUsage and comparison of artificial intelligence algorithms for determination of growth and development by cervical vertebrae stages in orthodonticsen_US
dc.typeArticleen_US

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