The removal of the third molar is one of the most frequently performed surgical procedures in oral surgery. Deep-learning is an encouraging approach to segment anatomical structures and later on in clinical decision making, though further enhancement of the algorithm is advised to improve the accuracy. Dice-coefficients were calculated to quantify the degree of similarity between the manually and automatically segmented M3s and IAN. Subsequently, the trained U-net was applied onto the original OPGs to detect and segment both structures. A deep-learning approach based on U-net was applied on the reference data to train the convolutional neural network (CNN) in the detection and segmentation of the M3 and IAN. As a reference, M3s and IAN were segmented manually on 81 OPGs. In this study, we developed and validated an automated approach, based on deep-learning, to detect and segment the M3 and IAN on OPGs. To assess this risk, the identification of M3 and IAN on dental panoramic radiographs (OPG) is mandatory. The approximity of the inferior alveolar nerve (IAN) to the roots of lower third molars (M3) is a risk factor for the occurrence of nerve damage and subsequent sensory disturbances of the lower lip and chin following the removal of third molars.

Panoramic radiograph scan cdr dicom