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Updated: Oct 15, 2025
Systematic Assessment of Mammalian Skull Specimens for Dental and Temporomandibular Joint Pathology
Published on: August 22, 2022
Deep learning for osteoarthritis classification in temporomandibular joint
Won Jung1,2, Kyung-Eun Lee1,2, Bong-Jik Suh1,2
1Department of Oral Medicine, School of Dentistry, Jeonbuk National University, Jeonju, Korea.
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.
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