Deep learning method to automatically diagnose periodontal bone loss and periodontitis stage in dental panoramic radiograph

• Published in: Journal of dentistry Vol. 150; p. 105373
• Main Authors: Xue, Ting, Chen, Lei, Sun, Qinfeng
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.11.2024
• Subjects: Algorithms; Alveolar Bone Loss - diagnostic imaging; Computer-aided diagnosis; Deep Learning; Humans; Male; Neural Networks, Computer; Periodontal bone loss; Periodontitis - diagnostic imaging; Periodontitis stage; Radiography, Panoramic; Deep learning; Periodontal bone loss; Computer-aided diagnosis; Periodontitis stage
• ISSN: 0300-5712; 1879-176X; 1879-176X
• DOI: 10.1016/j.jdent.2024.105373

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. 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. 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%. 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. 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.