Chau Ka-Kei, Zhu Meilu, AlHadidi Abeer, Wang Cheng, Hung Kuofeng, Wohlgemuth Pierre, Lam Walter Yu Hang, Liu Weicai, Yuan Yixuan, Chen Hui
Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, PR China.
Department of Mechanical Engineering, City University of Hong Kong, Hong Kong SAR, PR China.
J Dent. 2025 Feb;153:105526. doi: 10.1016/j.jdent.2024.105526. Epub 2024 Dec 10.
Periapical lesions are not always evident on radiographic scans. Sometimes, asymptomatic or initial periapical lesions on cone-beam computed tomography (CBCT) could be missed by inexperienced dentists, especially when the scan has a large field of view and is not for endodontic treatment purposes. Previously, numerous algorithms have been introduced to assist radiographic assessment and diagnosis in the field of endodontics. This study aims to investigate the efficacy of CBCT-SAM, a new artificial intelligence (AI) model, in identifying periapical lesions on CBCT.
Model training and validation in this study was performed using 185 CBCT scans with confirmed periapical lesions. Manual segmentation labels were prepared by a trained operator and validated by a maxillofacial radiologist. The diagnostic and segmentation performances of four AI models were evaluated and compared: CBCT-SAM, CBCT-SAM without progressive Prediction Refinement Module (PPR), and two previously developed models: Modified U-Net and PAL-Net. Accuracy was used to evaluated the diagnostic performance of the models, and accuracy, sensitivity, specificity, precision and Dice Similarity Coefficient (DSC) were used to evaluate the models' segmentation performance.
CBCT-SAM achieved an average diagnostic accuracy of 98.92% ± 010.37% and an average segmentation accuracy of 99.65% ± 0.66%. The average sensitivity, specificity, precision and DSC were 72.36 ± 21.61%, 99.87% ± 0.11%, 0.73 ± 0.21 and 0.70 ± 0.19. CBCT-SAM and PAL-Net performed significantly better than Modified U-Net in segmentation accuracy (p = 0.023, p = 0.041), sensitivity (p = 0.000, p = 0.002), and DSC (p= 0.001, p= 0.004). There is no significant difference between CBCT-SAM, CBCT-SAM without PPR and PAL-Net. However, with PPR incorporated into the model, CBCT-SAM slightly surpassed PAL-Net in the diagnostic and segmentation tasks.
CBCT-SAM is capable of providing expert-level assistance in the identification of periapical lesions on CBCT.
The application of artificial intelligence could increase dentists' chairside diagnostic accuracy and efficiency. By assisting radiographic assessment, such as periapical lesions on CBCT, it helps reduce the chance of missed diagnosis by human errors and facilitates early detection and treatment of dental pathologies at the early stage.
根尖周病变在影像学扫描中并不总是明显可见。有时，锥形束计算机断层扫描（CBCT）上无症状或早期的根尖周病变可能会被经验不足的牙医漏诊，尤其是当扫描视野较大且并非用于牙髓治疗目的时。此前，在牙髓病学领域已经引入了许多算法来辅助影像学评估和诊断。本研究旨在探讨一种新的人工智能（AI）模型CBCT-SAM在识别CBCT上根尖周病变方面的有效性。
本研究使用185例确诊有根尖周病变的CBCT扫描进行模型训练和验证。由一名经过培训的操作人员准备手动分割标签，并由一名颌面放射科医生进行验证。评估并比较了四种AI模型的诊断和分割性能：CBCT-SAM、没有渐进式预测优化模块（PPR）的CBCT-SAM，以及两种先前开发的模型：改良U-Net和PAL-Net。使用准确率评估模型的诊断性能，使用准确率、灵敏度、特异度、精度和骰子相似系数（DSC）评估模型的分割性能。
CBCT-SAM的平均诊断准确率为98.92%±10.37%，平均分割准确率为99.65%±0.66%。平均灵敏度、特异度、精度和DSC分别为72.36±21.61%、99.87%±0.11%、0.73±0.21和0.70±0.19。在分割准确率（p = 0.023，p = 0.041）、灵敏度（p = 0.000，p = 0.002）和DSC（p = 0.001，p = 0.004）方面，CBCT-SAM和PAL-Net的表现明显优于改良U-Net。CBCT-SAM、没有PPR的CBCT-SAM和PAL-Net之间没有显著差异。然而，将PPR纳入模型后，CBCT-SAM在诊断和分割任务中略超过PAL-Net。
CBCT-SAM能够在识别CBCT上的根尖周病变方面提供专家级别的协助。
人工智能的应用可以提高牙医在椅旁的诊断准确性和效率。通过辅助影像学评估，如CBCT上的根尖周病变，有助于减少人为错误导致漏诊的机会，并促进牙科疾病在早期的早期发现和治疗。
