Using a neural network for supporting radiographic diagnosis of dental caries

Abstract

This study uses an artificial intelligent model, a radial basis function neural network (RBF), to support radiography diagnosis of dental caries. One hundred and sixty radiography images of proximal faces of extracted human teeth were analyzed by 25 examiners, which diagnosed the presence or absence of dental caries. The same teeth were then subjected to optical microscope analysis, which allowed the verification of their actual conditions. Such information was classified as gold standards, and was employed to training a neural network to diagnose caries by means of radiography images. In order to verify the network's ability to diagnose new cases, data were organized in two subgroups: a training subgroup and a test subgroup. Receiver operating characteristics (ROC) curves allowed the comparison between diagnosis efficacy with or without the use of a neural network, showing that the adopted artificial intelligent model significantly improved diagnosis qualities.