Automatic Identification of Relationship between Tooth Root and Mandibular Canal Based on One Step Deep Neural Network

To improve the accuracy and efficiency of identifying the relationship between the root of the impacted mandibular third molar (M3M) and the mandibular canal in panoramic radiographs, we proposed an automatic method based on a deep convolutional neural network. This method treats the automatic identification of the relationship between the root of the M3M and the mandibular canal as a combination of regression and classification tasks. It uses the YOLOv5 (You Only Look Once) network as a framework for constructing a deep convolutional neural network that can accomplish detection and classification tasks simultaneously. This network, which takes the spatial relationship information extracted from the corresponding cone-beam CT images as the ground-truth, was trained to learn the nonlinear relationship between image features and the root of the M3M contacting the mandibular canal. When inputting a newly acquired panoramic radiograph into the trained network, the network will output the probability value for the root of the M3M contacting the mandibular canal. In the meantime, the region that includes the root of the M3M contacting the mandibular canal can be predicted. The experimental results show that the proposed method can provide an accurate judgment of whether the roots of impacted mandibular wisdom teeth in the panoramic radiographs are in contact with the mandibular canal and the location of regions in which the roots of the M3M are in contact with the mandibular canals; compared to manual diagnosis and the other methods, the proposed method can obtain more accurate results.