Eser, GozdeDuman, Suayip BurakBayrakdar, Ibrahim SevkiCelik, Ozer2024-08-042024-08-0420230305-182X1365-2842https://doi.org/10.1111/joor.13481https://hdl.handle.net/11616/101369BackgroundThe 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.eninfo:eu-repo/semantics/openAccessartificial intelligencecone beam computed tomographyosteoarthritistemporomandibular disorderstemporomandibular jointArticle5097587663718640010.1111/joor.134812-s2.0-85160089914Q1WOS:000994044400001Q1