Quantitative level determination of fixed restorations on panoramic radiographs using deep learning.

Aim: Although many studies in various fields employ deep learning models, only a few such studies exist in dental imaging. The present article aims to evaluate the effectiveness of convolutional neural network (CNN) algorithms for the detection and diagnosis of the quantitative level of dental restorations using panoramic radiographs by preparing a novel dataset.

Materials And Methods: 20,973 panoramic radiographs were used, all labeled into five distinct categories by three dental experts.