Deep learning system to predict the three‐dimensional contact status between the mandibular third molar and mandibular canal using panoramic radiographs

Abstract Aim We aim to evaluate the diagnostic performance of a deep learning (DL) system for determining the three‐dimensional contact status between the mandibular third molar and canal on panoramic radiography images. Methods A total of 800 image patches consisting of 400 patches of low‐ and high‐risk groups, each verified by computed tomography (CT) or cone‐beam CT for dental use, were cropped from downloaded panoramic images and input into a DL system. Seven hundred of these patches (350 high‐risk and 350 low‐risk group patches) were randomly assigned to the training and validation datasets, and 100 (50 high‐risk and 50 low‐risk group patches) were assigned to the test datasets. Using data augmentation for the training datasets, the training process was carried out twice. Receiver operating characteristic (ROC) analysis was used to compare the performance of two kinds of observers (residents and radiologists) with the same test images. The interclass correlation coefficients (ICCs) were determined to evaluate the diagnostic consistency. Results The area under the ROC curves (AUCs) of the DL model, residents, and radiologists were 0.85, 0.55, and 0.81, respectively. Significant differences were observed between the DL model and residents, and between the residents and radiologists. The ICCs of the DL model, residents, and radiologists were 0.69, 0.19, and 0.54, respectively. Conclusions The DL model has potential for use in diagnostic support in the evaluation of the three‐dimensional contact status between the mandibular third molar and canal on panoramic images.