Abstract: Objective: To explore the value of convolutional neural network in the diagnosis of displaced root fracture (DRF) based on cone-beam CT (CBCT). Methods: The CBCT images of 286 patients with DRF were collected in our hospital. The regions of interest were obtained by automatic and manual cropping, and labeled as positive or negative manually. ResNet 50 was used for training and establishing diagnosis models for DRF. The diagnosis efficiency of two models was calculated (accuracy, specificity, and sensitivity). Results: One hundred and sixty-four patients' images were used for automatic cropping and 122 patients' images were used for manual cropping. In the automatic cropping group, there were 184 positive images and 186 negative images in the training set; 64 positive images and 66 negative images in the test set. While in the manual cropping group, there were 96 positive images and 114 negative images in the training set; 32 positive images and 36 negative images in the test set. The accuracy, specificity, and sensitivity were 98.5%, 100%, and 96.9% with manual cropping; while they were 90.0%, 95.5%, and 84.4% with automatic cropping. Conclusion: Convolutional neural network ResNet 50 could be used to detect displaced root fractures with high diagnosis efficiency.

Key words: displaced root fractures, convolutional neural network, cone beam CT