Classification of temporomandibular joint osteoarthritis on cone beam computed tomography images using artificial intelligence system

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dc.authoridBAYRAKDAR, Ibrahim Sevki/0000-0001-5036-9867
dc.authoridEser, Gozde/0000-0003-4170-7929
dc.authoridduman, suayip burak/0000-0003-2552-0187
dc.authoridCelik, Ozer/0000-0002-4409-3101
dc.authorwosidBAYRAKDAR, Ibrahim Sevki/Y-1232-2019
dc.authorwosidEser, Gozde/ADR-8081-2022
dc.authorwosidduman, suayip burak/ABE-5878-2020
dc.contributor.authorEser, Gozde
dc.contributor.authorDuman, Suayip Burak
dc.contributor.authorBayrakdar, Ibrahim Sevki
dc.contributor.authorCelik, Ozer
dc.date.accessioned2024-08-04T20:53:44Z
dc.date.available2024-08-04T20:53:44Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractBackgroundThe use of artificial intelligence has many advantages, especially in the field of oral and maxillofacial radiology. Early diagnosis of temporomandibular joint osteoarthritis by artificial intelligence may improve prognosis. ObjectiveThe aim of this study is to perform the classification of temporomandibular joint (TMJ) osteoarthritis and TMJ segmentation on cone beam computed tomography (CBCT) sagittal images with artificial intelligence. ResultsThe sensitivity, precision and F1 scores of the model for TMJ osteoarthritis classification are 1, 0.7678 and 0.8686, respectively. The accuracy value for classification is 0.7678. The prediction values of the classification model are 88% for healthy joints, 70% for flattened joints, 95% for joints with erosion and 86% for joints with osteophytes. The sensitivity, precision and F1 score of the YOLOv5 model for TMJ segmentation are 1, 0.9953 and 0.9976, respectively. The AUC value of the model for TMJ segmentation is 0.9723. In addition, the accuracy value of the model for TMJ segmentation was found to be 0.9953. ConclusionArtificial intelligence model applied in this study can be a support method that will save time and convenience for physicians in the diagnosis of the disease with successful results in TMJ segmentation and osteoarthritis classification.en_US
dc.description.sponsorshipEskisehir Osmangazi University Scientific Research Projects Coordination Unit [202045E06]en_US
dc.description.sponsorshipThis work was supported by the Eskisehir Osmangazi University Scientific Research Projects Coordination Unit (grant number 202045E06).en_US
dc.identifier.doi10.1111/joor.13481
dc.identifier.endpage766en_US
dc.identifier.issn0305-182X
dc.identifier.issn1365-2842
dc.identifier.issue9en_US
dc.identifier.pmid37186400en_US
dc.identifier.scopus2-s2.0-85160089914en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage758en_US
dc.identifier.urihttps://doi.org/10.1111/joor.13481
dc.identifier.urihttps://hdl.handle.net/11616/101369
dc.identifier.volume50en_US
dc.identifier.wosWOS:000994044400001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofJournal of Oral Rehabilitationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectartificial intelligenceen_US
dc.subjectcone beam computed tomographyen_US
dc.subjectosteoarthritisen_US
dc.subjecttemporomandibular disordersen_US
dc.subjecttemporomandibular jointen_US
dc.titleClassification of temporomandibular joint osteoarthritis on cone beam computed tomography images using artificial intelligence systemen_US
dc.typeArticleen_US

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