A deep learning approach to dental restoration classification from bitewing and periapical radiographs.

Objective: The aim of this study was to examine the success of deep learning-based convolutional neural networks (CNN) in the detection and differentiation of amalgam, composite resin, and metal-ceramic restorations from bitewing and periapical radiographs.

Method And Materials: Five hundred and fifty bitewing and periapical radiographs were used. Eighty percent of the images were used for training, and 20% were left for testing.