In this work, we aim to develop and validate an AI-assisted method for identifying the history of root canal therapy by using periapical films. First, we propose a pre-processing method to extract the regions of interest (ROI) containing the root canals. Then, in order to improve the generalization ability, data augmentation is adopted to expand the dataset. Finally, three machine learning methods, including SIFT-SVM, CNN, and transfer learning are used. All the models are validated based on the receiving operating characteristic (ROC) curve analysis. The accuracies for the three machine learning methods are above 95%. The AUC, sensitivity, and specificity of the best model are also presented and analyzed.

在这项工作中，我们的目标是开发和验证一种人工智能辅助方法，用于通过使用根尖膜来识别根管治疗的历史。首先，我们提出了一种预处理方法来提取包含根管的感兴趣区域（ROI）。然后，为了提高泛化能力，采用数据增强来扩展数据集。最后，使用了三种机器学习方法，包括 SIFT-SVM、CNN 和迁移学习。所有模型均基于接收操作特性（ROC）曲线分析进行验证。三种机器学习方法的准确率均在 95% 以上。还介绍和分析了最佳模型的 AUC、敏感性和特异性。