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
Department of Periodontics, Division of Dentistry, Taoyuan Chang Gung Memorial Hospital, Taoyuan City 333423, Taiwan.
Department of Operative Dentistry, Taoyuan Chang Gung Memorial Hospital, Taoyuan City 333423, Taiwan.
Diagnostics (Basel). 2024 Aug 4;14(15):1687. doi: 10.3390/diagnostics14151687.
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.
牙周炎的严重程度可通过计算牙槽嵴（ALC）水平以及牙齿骨质与牙骨质-釉质界（CEJ）之间的骨质流失水平来分析。然而，牙医需要在根尖片（PA）上手动标记症状以评估骨质流失，这一过程既耗时又容易出错。本研究提出了以下有助于疾病评估并减少误差的新方法。首先，采用创新的牙周炎图像增强方法来提高PA图像质量。随后，通过目标检测可从PA图像中准确提取单颗牙齿，最高准确率可达97.01%。本研究开发的实例分割可准确提取感兴趣区域，能够生成牙齿骨质和牙冠的掩膜，准确率分别为93.48%和96.95%。最后，提出了一种新颖的检测算法，可自动标记有症状牙齿的CEJ和ALC，便于牙医更快地准确评估骨质流失的严重程度。本研究使用的PA图像数据库由台湾长庚医疗中心提供，IRB编号为02002030B0，通过本研究开发的技术显著减少了牙科诊断所需的时间并提高了医疗质量。
