Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, Nagoya, Japan.
Division of Radiological Technology, Dental Hospital, Aichi-Gakuin University, Nagoya, Japan.
Sci Rep. 2021 Aug 6;11(1):16044. doi: 10.1038/s41598-021-95653-9.
Although panoramic radiography has a role in the examination of patients with cleft alveolus (CA), its appearances is sometimes difficult to interpret. The aims of this study were to develop a computer-aided diagnosis system for diagnosing the CA status on panoramic radiographs using a deep learning object detection technique with and without normal data in the learning process, to verify its performance in comparison to human observers, and to clarify some characteristic appearances probably related to the performance. The panoramic radiographs of 383 CA patients with cleft palate (CA with CP) or without cleft palate (CA only) and 210 patients without CA (normal) were used to create two models on the DetectNet. The models 1 and 2 were developed based on the data without and with normal subjects, respectively, to detect the CAs and classify them into with or without CP. The model 2 reduced the false positive rate (1/30) compared to the model 1 (12/30). The overall accuracy of Model 2 was higher than Model 1 and human observers. The model created in this study appeared to have the potential to detect and classify CAs on panoramic radiographs, and might be useful to assist the human observers.
尽管全景放射摄影术在牙槽裂（CA）患者的检查中具有一定作用，但有时其表现难以解读。本研究旨在开发一种计算机辅助诊断系统，用于使用深度学习目标检测技术，在学习过程中有无正常数据的情况下，对全景片上的 CA 状况进行诊断，验证其与人类观察者相比的性能，并阐明一些可能与性能相关的特征表现。使用了 383 例腭裂（CP 合并 CA）或单纯牙槽裂（仅 CA）患者和 210 例无 CA（正常）患者的全景片，来在 DetectNet 上创建两个模型。模型 1 和 2 分别基于无正常人和有正常人的数据进行开发，以检测 CA 并将其分类为合并或不合并 CP。与模型 1（12/30）相比，模型 2 降低了假阳性率（1/30）。模型 2 的整体准确率高于模型 1 和人类观察者。本研究中创建的模型似乎具有在全景片上检测和分类 CA 的潜力，可能有助于辅助人类观察者。
