Dental explorer and dental radiographs are mainly used for the examination of dental caries, however they tend to depend on the experience of the examiner in the end and the reliability of the examiner may not be satisfactory. To overcome this problem, we tried to improve the diagnostic accuracy of dental caries by applying the deep learning which processes images or recognizes patterns and has superior performance in image analysis. This study was approved by the Institutional Review Board of the Graduate School of Dentistry, Seoul National University using the search engine scraping method for collecting tooth images in the internet search engine. From April 2019 to May 2019 it has been going on for about two months. Tooth images were divided into sound teeth, caries teeth, and treated teeth according to WHO guidelines. Finally, 70 images were collected respectively, and 50 training sets for learning the deep learning model and 20 test sets for verifying the training set were randomly assigned. ROC curve, AUC, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were calculated to confirm reliability in the classification model of sound teeth and caries teeth, and, the model for classifying sound teeth, caries teeth, treated teeth confirmed the accuracy of the classification. The following conclusion was obtained from the experiment. 1. In order to extract the characteristics of the dental image and establish a reliable model, it should be designed as a deep neural network.2. In the model dividing sound teeth and caries teeth, the transfer learning model showed a much higher accuracy (90.0%, AUC: 0.963) than the non-transfer learning model (50.0%).3. Accuracy was reduced (61.7%) in the classification of sound teeth, caries teeth, and treated teeth. The accuracy of each image was 80.0% for treatment, 35.0% for carious teeth, 25.0% for sound teeth, respectively.
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