Dental implant brand and angle identification using deep neural networks

Statement of problem: Determining the brand and angle of an implant clinically or radiographically can be challenging. Whether artificial intelligence can assist is unclear. Purpose: The purpose of the present study was to determine the brand and angle of implants from panoramic radiographs with artificial intelligence. Material and methods: Panoramic radiographs were used to classify the accuracy of different dental implant brands through deep convolutional neural networks (CNNs) with transfer-learning strategies. The implant classification performance of 5 deep CNN models was evaluated using a total of 11 904 images of 5 different implant types extracted from 2634 radiographs. In addition, the angle of implant images was estimated by calculating the angle of 2634 implant images by applying a regression model based on deep CNN. Results: Among the 5 deep CNN models, the highest performance was obtained in the Visual Geometry Group (VGG)-19 model with a 98.3% accuracy rate. By applying a fusion approach based on majority voting, the accuracy rate was slightly improved to 98.9%. In addition, the root mean square error value of 2.91 degrees was obtained as a result of the regression model used in the implant angle estimation problem. Conclusions: Implant images from panoramic radiographs could be classified with a high accuracy, and their angles estimated with a low mean error. © 2025 Editorial Council for The Journal of Prosthetic Dentistry