Detection of Dental Caries Risk Detection Using Deep Learning

Abstract

Dental caries is the most common oral diseases affecting individual worldwide , necessitating the development of advanced diagnostic methods. This study introduces a novel approach using a score based multi-input deep  neural network convolution(CNN) for the effective detection of dental caries. Our model attempts to enhances the accuracy of caries detection and help dental professionals make well-informed treatment decisions by utilizing a variety of input data, such as radiographic images and clinical parameters.  When tested on a large dataset, the suggested methodology outperformed conventional methods.  According to the results, combining multiple input modalities can greatly enhance dental practice's diagnostic results.  This study opens the door for future advancements in caries management and prevention in addition to making a contribution to the field of dental diagnostics.