Annotated Data set.zip
Dental radiology has significantly benefited from cone beam computed tomography (CBCT) because of its compact size and low radiation exposure. Canal tracking is an important application of CBCT for determining the relationship between the inferior alveolar nerve and the third molar. Usually, canal tacking is performed manually, which takes a lot of time. This study aims to develop an artificial intelligence (AI) model for automating the classification of the mandibular canal in relation to the third molar. Methods: A retrospective study was conducted using CBCT images. 3D slicer software was used to annotate and classify data into lingual, buccal, and inferior categories. Two CNN models, AlexNet and ResNet50, were developed for classifying the relationship. The study included 262 images for training and 172 for testing, with model performance evaluated by sensitivity, precision, and F1 score.