Deep learning for osteoarthritis classification in temporomandibular joint

Objectives This study aimed to develop a diagnostic support tool using pretrained models for classifying panoramic images of the temporomandibular joint (TMJ) into normal and osteoarthritis (OA) cases. Subjects and Methods A total of 858 panoramic images of the TMJ (395 normal and 463 TMJ‐OA) were obtained from 518 individuals from January 2015 to December 2018. The data were randomly divided into training, validation, and testing sets (6:2:2). We used pretrained Resnet152 and EfficientNet‐B7 as transfer learning models. The accuracy, specificity, sensitivity, area under the curve, and gradient‐weighted class activation mapping (grad‐CAM) of both trained models were evaluated. The performances of the trained models were compared to that of dentists (both TMD specialists and general dentists). Results The classification accuracies of ResNet‐152 and EfficientNet‐B7 were 0.87 and 0.88, respectively. The trained models exhibited the highest accuracy in OA classification. In the grad‐CAM analysis, the trained models focused on specific areas in osteoarthritis images where erosion or osteophyte were observed. Conclusions The artificial intelligence model improved the diagnostic power of TMJ‐OA when trained with two‐dimensional panoramic condyle images and can be effectively applied by dentists as a screening diagnostic tool for TMJ‐OA.