Oral diseases · 2021
Deep learning for osteoarthritis classification in temporomandibular joint.
СтатьяРандомизированное исследованиеНазвание и аннотация: Перевод в очереди
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Jung Won, Lee Kyung-Eun, Suh Bong-Jik, Seok Hyun, Lee Dae-Woo
Аннотация
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.
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.