Evaluation of the Alveolar Crest and Cemento-Enamel Junction in Periodontitis Using Object Detection on Periapical Radiographs
Tai-Jung Lin,
Yi-Cheng Mao,
Yuan-Jin Lin,
Chin-Hao Liang,
Yi-Qing He,
Yun-Chen Hsu,
Shih-Lun Chen,
Tsung-Yi Chen,
Chiung-An Chen,
Kuo-Chen Li,
Patricia Angela R. Abu
Affiliations
Tai-Jung Lin
Department of Periodontics, Division of Dentistry, Taoyuan Chang Gung Memorial Hospital, Taoyuan City 333423, Taiwan
Yi-Cheng Mao
Department of Operative Dentistry, Taoyuan Chang Gung Memorial Hospital, Taoyuan City 333423, Taiwan
Yuan-Jin Lin
Department of Program on Semiconductor Manufacturing Technology, Academy of Innovative Semiconductor and Sustainable Manufacturing, National Cheng Kung University, Tainan City 701401, Taiwan
Chin-Hao Liang
Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 320234, Taiwan
Yi-Qing He
Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 320234, Taiwan
Yun-Chen Hsu
Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 320234, Taiwan
Shih-Lun Chen
Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 320234, Taiwan
Tsung-Yi Chen
Department of Electronic Engineering, Feng Chia University, Taichung City 407301, Taiwan
Chiung-An Chen
Department of Electrical Engineering, Ming Chi University of Technology, New Taipei City 243303, Taiwan
Kuo-Chen Li
Department of Information Management, Chung Yuan Christian University, Taoyuan City 320317, Taiwan
Patricia Angela R. Abu
Ateneo Laboratory for Intelligent Visual Environments, Department of Information Systems and Computer Science, Ateneo de Manila University, Quezon City 1108, Philippines
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.
