Evaluation of the Alveolar Crest and Cemento-Enamel Junction in Periodontitis Using Object Detection on Periapical Radiographs

• Main Authors: Lin, Tai Jung, Mao, Yi Cheng, Lin, Yuan Jin, Liang, Chin Hao, He, Yi Qing, Hsu, Yun Chen, Chen, Shih Lun, Chen, Tsung Yi, Chen, Chiung An, Li, Kuo Chen, Abu, Patricia Angela R
• 格式: text
• 出版: Archīum Ateneo 2024
• 主題: alveolar crest; apical periodontitis; cemento-enamel junction; Mask R-CNN; object detection; Analytical, Diagnostic and Therapeutic Techniques and Equipment; Biomedical; Biomedical Engineering and Bioengineering; Electrical and Computer Engineering; Engineering; Medicine and Health Sciences
• 在線閱讀: https://archium.ateneo.edu/intelligent-visual-env/2; https://archium.ateneo.edu/context/intelligent-visual-env/article/1001/viewcontent/diagnostics_14_01687.pdf
• 機構: Ateneo De Manila University

The severity of periodontitis can be analyzed by calculating the loss of alveolar crest (ALC) level and the level of bone loss between the tooth’s bone and the cemento-enamel junction (CEJ). However, dentists need to manually mark symptoms on periapical radiographs (PAs) to assess bone loss, a process that is both time-consuming and prone to errors. This study proposes the following new method that contributes to the evaluation of disease and reduces errors. Firstly, innovative periodontitis image enhancement methods are employed to improve PA image quality. Subsequently, single teeth can be accurately extracted from PA images by object detection with a maximum accuracy of 97.01%. An instance segmentation developed in this study accurately extracts regions of interest, enabling the generation of masks for tooth bone and tooth crown with accuracies of 93.48% and 96.95%. Finally, a novel detection algorithm is proposed to automatically mark the CEJ and ALC of symptomatic teeth, facilitating faster accurate assessment of bone loss severity by dentists. The PA image database used in this study, with the IRB number 02002030B0 provided by Chang Gung Medical Center, Taiwan, significantly reduces the time required for dental diagnosis and enhances healthcare quality through the techniques developed in this research.