Predicting the Debonding of CAD/CAM Composite Resin Crowns with AI
Predicting the Debonding of CAD/CAM Composite Resin Crowns with AI
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DOI：
10.1177/0022034519867641
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发表时间：
2019-08-03
影响因子：
7.6
通讯作者：
Imazato, S.
中科院分区：
文献类型：
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作者：
Yamaguchi, S.;Lee, C.;Imazato, S.
对于脱粘的预防措施尚未建立，并且非常期望提高计算机辅助设计/计算机辅助制造（CAD/CAM）复合树脂（CR）冠的存活率。本研究的目的是评估深度学习与卷积神经网络（CNN）方法的有效性，以预测CAD/CAM CR牙冠从三维（3D）口腔扫描仪扫描的模具立体光刻模型捕获的二维图像的脱粘概率。所有CAD/CAM CR牙冠均于2014年4月至2015年11月在大坂大学牙科医院修复科生产（大坂大学伦理审查委员会，批准H27-E11）。数据集共包括24例病例：12例无故障，12例剥离为已知标签。总共8，640张图像被随机分为6，480张训练和验证图像以及2，160张测试图像。使用CNN方法进行深度学习，以开发学习模型来预测脱粘概率。对测试图像的预测准确度、精确度、召回率、F-测量、接收器操作特性和学习模型的曲线下面积进行评估。此外，在测试图像的预测期间测量平均计算时间。使用CNN方法进行深度学习预测脱粘概率的预测准确率、精确率、召回率和F测量值分别为98.5%、97.0%、100%和0.985。对于2，160个测试图像，平均计算时间为2 ms/步。曲线下面积为0.998。人工智能（AI）技术-即本研究中建立的CNN方法的深度学习-在预测CAD/CAM CR冠的脱粘概率方面表现出相当好的性能，该冠具有从患者扫描的模具的3D立体光刻模型。
A preventive measure for debonding has not been established and is highly desirable to improve the survival rate of computer-aided design/computer-aided manufacturing (CAD/CAM) composite resin (CR) crowns. The aim of this study was to assess the usefulness of deep learning with a convolution neural network (CNN) method to predict the debonding probability of CAD/CAM CR crowns from 2-dimensional images captured from 3-dimensional (3D) stereolithography models of a die scanned by a 3D oral scanner. All cases of CAD/CAM CR crowns were manufactured from April 2014 to November 2015 at the Division of Prosthodontics, Osaka University Dental Hospital (Ethical Review Board at Osaka University, approval H27-E11). The data set consisted of a total of 24 cases: 12 trouble-free and 12 debonding as known labels. A total of 8,640 images were randomly divided into 6,480 training and validation images and 2,160 test images. Deep learning with a CNN method was conducted to develop a learning model to predict the debonding probability. The prediction accuracy, precision, recall, F-measure, receiver operating characteristic, and area under the curve of the learning model were assessed for the test images. Also, the mean calculation time was measured during the prediction for the test images. The prediction accuracy, precision, recall, and F-measure values of deep learning with a CNN method for the prediction of the debonding probability were 98.5%, 97.0%, 100%, and 0.985, respectively. The mean calculation time was 2 ms/step for 2,160 test images. The area under the curve was 0.998. Artificial intelligence (AI) technology-that is, the deep learning with a CNN method established in this study-demonstrated considerably good performance in terms of predicting the debonding probability of a CAD/CAM CR crown with 3D stereolithography models of a die scanned from patients.