Using Deep Learning to Detect Tooth Decay in Children-To what extent can a convolutional neural network be used to detect tooth decay in images of children’s teeth accurately?

Abstract

Approximately 530 million children have untreated cavities, which could affect future permanent teeth if not treated well. Researchers have enhanced X-ray detection of tooth decay to improve the detection of cavities. However, dentophobia and lack of insurance and dentist availability are barriers that constrain thousands from receiving proper dental care. This study used a deep learning convolutional neural network model to address this problem to detect cavities. Three hundred twenty-two photos taken by a camera of child patients were used. The dataset was classified into two classes: cavity and no cavity. The MobileNetV2 architecture was used for feature extraction and cavity detection. The model was then trained and tested so that it classified photos into two categories. The model achieved an accuracy of 93.5%.