牙槽
射线照相术
牙周炎
医学
牙科
卷积神经网络
医学诊断
接收机工作特性
分割
口腔正畸科
人工智能
放射科
计算机科学
内科学
作者
Chun‐Teh Lee,Tanjida Kabir,Jiman Nelson,Sally Sheng,Hsiu-Wan Meng,Thomas E. Van Dyke,Muhammad F. Walji,Xiaoqian Jiang,Soheil Shams
摘要
The goal was to use a deep convolutional neural network to measure the radiographic alveolar bone level to aid periodontal diagnosis.A deep learning (DL) model was developed by integrating three segmentation networks (bone area, tooth, cemento-enamel junction) and image analysis to measure the radiographic bone level and assign radiographic bone loss (RBL) stages. The percentage of RBL was calculated to determine the stage of RBL for each tooth. A provisional periodontal diagnosis was assigned using the 2018 periodontitis classification. RBL percentage, staging, and presumptive diagnosis were compared with the measurements and diagnoses made by the independent examiners.The average Dice Similarity Coefficient (DSC) for segmentation was over 0.91. There was no significant difference in the RBL percentage measurements determined by DL and examiners ( p=.65 ). The area under the receiver operating characteristics curve of RBL stage assignment for stages I, II, and III was 0.89, 0.90, and 0.90, respectively. The accuracy of the case diagnosis was 0.85.The proposed DL model provides reliable RBL measurements and image-based periodontal diagnosis using periapical radiographic images. However, this model has to be further optimized and validated by a larger number of images to facilitate its application.
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