Department of Biomaterials Science, Osaka University Graduate School of Dentistry, Suita, Japan.
Department of Prosthodontics, Faculty of Dentistry, Ankara University, Ankara, Turkey.
J Dent Res. 2019 Oct;98(11):1234-1238. doi: 10.1177/0022034519867641. Epub 2019 Aug 3.
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.
一种预防分层的措施尚未建立，因此提高计算机辅助设计/计算机辅助制造（CAD/CAM）复合树脂（CR）冠成活率是非常可取的。本研究旨在评估使用卷积神经网络（CNN）深度学习方法从通过 3D 口腔扫描仪扫描的模具的 3D 立体光刻模型捕获的 2 维图像预测 CAD/CAM CR 冠分层概率的有用性。所有 CAD/CAM CR 冠均于 2014 年 4 月至 2015 年 11 月在大阪大学牙科医院修复科（大阪大学伦理审查委员会，批准 H27-E11）制造。数据集共包括 24 例：12 例无故障，12 例分层。共 8640 张图像随机分为 6480 张训练和验证图像和 2160 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分层概率。评估了学习模型对测试图像的预测准确性、精度、召回率、F 值、接收器工作特征和曲线下面积。还测量了预测测试图像时的平均计算时间。CNN 方法进行深度学习对分层概率的预测的预测准确性、精度、召回率和 F 值分别为 98.5%、97.0%、100%和 0.985。对于 2160 张测试图像，平均计算时间为 2ms/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 方法深度学习-在预测从患者扫描的模具的 3D 立体光刻模型的 CAD/CAM CR 冠分层概率方面表现出相当好的性能。
