Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography

Objectives To apply a deep-learning system for diagnosis of maxillary sinusitis on panoramic radiography, and to clarify its diagnostic performance. Methods Training data for 400 healthy and 400 inflamed maxillary sinuses were enhanced to 6000 samples in each category by data augmentation. Image patches were input into a deep-learning system, the learning process was repeated for 200 epochs, and a learning model was created. Newly-prepared testing image patches from 60 healthy and 60 inflamed sinuses were input into the learning model, and the diagnostic performance was calculated. Receiver-operating characteristic (ROC) curves were drawn, and the area under the curve (AUC) values were obtained. The results were compared with those of two experienced radiologists and two dental residents. Results The diagnostic performance of the deep-learning system for maxillary sinusitis on panoramic radiographs was high, with accuracy of 87.5%, sensitivity of 86.7%, specificity of 88.3%, and AUC of 0.875. These values showed no significant differences compared with those of the radiologists and were higher than those of the dental residents. Conclusions The diagnostic performance of the deep-learning system for maxillary sinusitis on panoramic radiographs was sufficiently high. Results from the deep-learning system are expected to provide diagnostic support for inexperienced dentists.