Diagnosis of cystic lesions using panoramic and cone beam computed tomographic images based on deep learning neural network

Objectives: The aim of the current study was to evaluate the detection and diagnosis of three types of odontogenic cystic lesions (OCLs)—odontogenic keratocysts, dentigerous cysts, and periapical cysts—using dental panoramic radiography and cone beam computed tomographic (CBCT) images based on a deep convolutional neural network (CNN). Methods: The GoogLeNet Inception-v3 architecture was used to enhance the overall performance of the detection and diagnosis of OCLs based on transfer learning. Diagnostic indices (area under the ROC curve [AUC], sensitivity, specificity, and confusion matrix with and without normalization) were calculated and compared between pretrained models using panoramic and CBCT images. Results: The pretrained model using CBCT images showed good diagnostic performance (AUC = 0.914, sensitivity = 96.1%, specificity = 77.1%), which was significantly greater than that achieved by other models using panoramic images (AUC = 0.847, sensitivity = 88.2%, specificity = 77.0%) (p =.014). Conclusions: This study demonstrated that panoramic and CBCT image datasets, comprising three types of odontogenic OCLs, are effectively detected and diagnosed based on the deep CNN architecture. In particular, we found that the deep CNN architecture trained with CBCT images achieved higher diagnostic performance than that trained with panoramic images.