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Abstract：
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.
摘要：
牙周炎的严重程度可以通过计算牙槽骨和牙骨质-牙釉质交界处（CEJ）之间的牙槽峰（ALC）水平和骨丢失水平来分析。然而,牙医需要在根尖周X光片（PA）上手动标记症状以评估骨质流失，一个既耗时又容易出错的过程。这项研究提出了以下新方法，有助于疾病的评估并减少错误。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，目标检测可以从PA图像中准确提取单颗牙齿，最高准确率为97.01%。在这项研究中开发的实例分割准确地提取了感兴趣的区域，能够生成牙骨和牙冠面罩，准确率分别为93.48%和96.95%。最后,提出了一种新的检测算法来自动标记有症状牙齿的CEJ和ALC，促进牙医更快地准确评估骨质流失的严重程度。本研究中使用的PA图像数据库，长贡医疗中心提供的IRB编号为02002030B0，台湾,通过这项研究中开发的技术，显着减少了牙科诊断所需的时间，并提高了医疗保健质量。
