Journal of dentistry · 2024
Deep learning method to automatically diagnose periodontal bone loss and periodontitis stage in dental panoramic radiograph.
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Xue Ting, Chen Lei, Sun Qinfeng
Аннотация
OBJECTIVES: Artificial intelligence (AI) could be used as an automatically diagnosis method for dental disease due to its accuracy and efficiency. This research proposed a novel convolutional neural network (CNN)-based deep learning (DL) ensemble model for tooth position detection, tooth outline segmentation, tooth tissue segmentation, periodontal bone loss and periodontitis stage prediction using dental panoramic radiographs.
METHODS: The dental panoramic radiographs of 320 patients during the period January 2020 to December 2023 were collected in our dataset. All images were de-identified without private information. In total, 8462 teeth were included. The algorithms that DL ensemble model adopted include YOLOv8, Mask R-CNN, and TransUNet. The prediction results of DL method were compared with diagnosis of periodontists.
RESULTS: The periodontal bone loss degree deviation between the DL method and ground truth drawn by the three periodontists was 5.28%. The overall PCC value of the DL method and the periodontists' diagnoses was 0.832 (P < 0.001). The ICC value was 0.806 (P < 0.001). The total diagnostic accuracy of the DL method was 89.45%.
CONCLUSIONS: The proposed DL ensemble model in this study shows high accuracy and efficiency in radiographic detection and a valuable adjunct to periodontal diagnosis. The method has strong potential to enhance clinical professional performance and build more efficient dental health services.
CLINICAL SIGNIFICANCE: The DL method not only could help dentists for rapid and accurate auxiliary diagnosis and prevent medical negligence, but also could be used as a useful learning resource for inexperienced dentists and dental students.
METHODS: The dental panoramic radiographs of 320 patients during the period January 2020 to December 2023 were collected in our dataset. All images were de-identified without private information. In total, 8462 teeth were included. The algorithms that DL ensemble model adopted include YOLOv8, Mask R-CNN, and TransUNet. The prediction results of DL method were compared with diagnosis of periodontists.
RESULTS: The periodontal bone loss degree deviation between the DL method and ground truth drawn by the three periodontists was 5.28%. The overall PCC value of the DL method and the periodontists' diagnoses was 0.832 (P < 0.001). The ICC value was 0.806 (P < 0.001). The total diagnostic accuracy of the DL method was 89.45%.
CONCLUSIONS: The proposed DL ensemble model in this study shows high accuracy and efficiency in radiographic detection and a valuable adjunct to periodontal diagnosis. The method has strong potential to enhance clinical professional performance and build more efficient dental health services.
CLINICAL SIGNIFICANCE: The DL method not only could help dentists for rapid and accurate auxiliary diagnosis and prevent medical negligence, but also could be used as a useful learning resource for inexperienced dentists and dental students.