Research on deep learning assisted diagnosis technology of jaw lesions using panoramic radiographs

Objective To study the effect of deep learning applied to the assisted diagnosis of radiolucent lesions and radiopaque lesions of the jaws in panoramic radiography and to reduce the missed diagnosis, with early screening to assist doctors to improve the diagnostic accuracy. Methods This study was approved by the Ethics Committee of the West China Stomatological Hospital of Sichuan University. The YOLO v8m-p2 neural network model was constructed with 443 panoramic images as a subject to read. The labeled images were divided into 354 training sets, 45 verification sets, and 44 test sets, which were used for model training, verification, and testing. Accuracy, recall, F-1 score, G score, and mAP50 were used to evaluate the detection performance of the model. Results 443 panoramic images covered the common benign lesions of the jaw, the number of radiolucent lesions of the jaw was 318, containing dentigerous cyst, odontogenic keratocyst, and ameloblastoma. The number of radiopaque lesions was 145, containing idiopathic osteosclerosis, odontoma, cementoma, and cemento-osseous dysplasia; the samples are well representative. The accuracy of the YOLO v8m-p2 neural network model in identifying jaw lesions was 0.887, and the recall, F-1 score, G score, and mAP50 were 0.860, 0.873, 0.873, and 0.863, respectively. The recall rates of dentigerous cyst, odontogenic keratocyst, and ameloblastoma were 0.833, 0.941, and 0.875, respectively. Conclusion YOLO v8m-p2 neural network model has good diagnostic performance in preliminary detection of radiolucent and radiopaque lesions of the jaws in panoramic radiography and multi-classification monitoring of radiolucent lesions of jaws, which can assist doctors to screen jaw diseases in panoramic radiography.