Abstract
BACKGROUND/PURPOSE: Proper implant-ridge classification is crucial for developing a dental implant treatment plan. This study aimed to verify the ability of MobileNet, an advanced deep learning model characterized by a lightweight architecture that allows for efficient model deployment on resource-constrained devices, to identify the implant-ridge relationship. MATERIALS AND METHODS: A total of 630 cone-beam computerized tomography (CBCT) slices from 412 patients were collected and manually classified according to Terheyden's definition, preprocessed, and fed to MobileNet for training under the conditions of limited datasets (219 slices, condition A) and full datasets (630 cases) without and with automatic gap filling (conditions B and C). RESULTS: The overall model accuracy was 84.00% in condition A and 95.28% in conditions B and C. In condition C, the accuracy rates ranged from 94.00 to 99.21%, with F1 scores of 89.36-100.00%, and errors due to unidentifiable bone-implant contact and miscellaneous reasons were eliminated. CONCLUSION: The MobileNet architecture was able to identify the implant-ridge classification on CBCT slices and can assist clinicians in establishing a reliable preoperative diagnosis and treatment plan for dental implants. These results also suggest that artificial intelligence-assisted implant-ridge classification can be performed in the setting of general dental practice.