A phantom study on the evaluation of the YOLOv8 deep learning model in the detection of tooth ankylosis on cone-beam computed tomography images.

This study aimed to assess the performance of a deep learning algorithm for detecting tooth ankylosis on cone-beam computed tomography (CBCT) scans. A dataset of CBCT scans taken from phantoms simulating tooth ankylosis, including 60 teeth and a total of 4971 axial sections, was used to design an ankylosis detection model using the YOLOv8 algorithm. The first dataset (42 teeth, 70%) with 3822 axial sections comprised the training dataset, while the second dataset (18 teeth, 30%) with 1149 axial sections comprised the test dataset. The YOLOv8 algorithm was used to optimize the precise and efficient detection of the ankylotic area. Training loss was monitored during the learning process. Also, precise optimizations such as adaptive learning rate were used to ensure model convergence. The dice, precision, recall, and F1 score were calculated to assess the model's performance. Training loss of the designed model reached < 10% after 100 training epochs. The model showed 80% precision, 82% dice, 90.9% recall, and 83.9% F1 score. The designed model using the YOLOv8 algorithm performed optimally in efficient and precise detection of tooth ankylosis on CBCT scans and could be integrated into the clinical workflow, aiding clinicians and radiologists. Future studies are recommended to focus on further refinement of this model using larger datasets, architectural optimization, combining in-vivo and in-vitro datasets for model generalizability, and comparing radiologists' and the model's performance.