An Automatic Grading System for Orthodontically Induced External Root Resorption Based on Deep Convolutional Neural Network

作者全名："Xu, Shuxi; Peng, Houli; Yang, Lanxin; Zhong, Wenjie; Gao, Xiang; Song, Jinlin"

作者地址："[Xu, Shuxi; Peng, Houli; Yang, Lanxin; Zhong, Wenjie; Gao, Xiang; Song, Jinlin] Chongqing Med Univ, Coll Stomatol, Chongqing 401147, Peoples R China; [Xu, Shuxi; Peng, Houli; Yang, Lanxin; Zhong, Wenjie; Gao, Xiang; Song, Jinlin] Chongqing Key Lab Oral Dis & Biomed Sci, Chongqing 401147, Peoples R China; [Xu, Shuxi; Peng, Houli; Yang, Lanxin; Zhong, Wenjie; Gao, Xiang; Song, Jinlin] Chongqing Municipal Key Lab Oral Biomed Engn Highe, Chongqing 401147, Peoples R China"

通信作者："Gao, X; Song, JL (通讯作者)，Chongqing Med Univ, Coll Stomatol, Chongqing 401147, Peoples R China.; Gao, X; Song, JL (通讯作者)，Chongqing Key Lab Oral Dis & Biomed Sci, Chongqing 401147, Peoples R China.; Gao, X; Song, JL (通讯作者)，Chongqing Municipal Key Lab Oral Biomed Engn Highe, Chongqing 401147, Peoples R China."

来源：JOURNAL OF IMAGING INFORMATICS IN MEDICINE

ESI学科分类：　

WOS号：WOS:001284805400042

JCR分区：　

影响因子：　

年份：2024

卷号：37

期号：4

开始页：1800

结束页：1811

文献类型：Article

关键词：Artificial intelligence; Convolutional neural network; Orthodontics; Root resorption; Diagnostic system

摘要："Orthodontically induced external root resorption (OIERR) is a common complication of orthodontic treatments. Accurate OIERR grading is crucial for clinical intervention. This study aimed to evaluate six deep convolutional neural networks (CNNs) for performing OIERR grading on tooth slices to construct an automatic grading system for OIERR. A total of 2146 tooth slices of different OIERR grades were collected and preprocessed. Six pre-trained CNNs (EfficientNet-B1, EfficientNet-B2, EfficientNet-B3, EfficientNet-B4, EfficientNet-B5, and MobileNet-V3) were trained and validated on the pre-processed images based on four different cross-validation methods. The performances of the CNNs on a test set were evaluated and compared with those of orthodontists. The gradient-weighted class activation mapping (Grad-CAM) technique was used to explore the area of maximum impact on the model decisions in the tooth slices. The six CNN models performed remarkably well in OIERR grading, with a mean accuracy of 0.92, surpassing that of the orthodontists (mean accuracy of 0.82). EfficientNet-B4 trained with fivefold cross-validation emerged as the final OIERR grading system, with a high accuracy of 0.94. Grad-CAM revealed that the apical region had the greatest effect on the OIERR grading system. The six CNNs demonstrated excellent OIERR grading and outperformed orthodontists. The proposed OIERR grading system holds potential as a reliable diagnostic support for orthodontists in clinical practice."

基金机构：Chongqing Municipal Health Commission Program

基金资助正文：No Statement Available