A Deep Learning Approach to Automatic Gingivitis Screening based Onclassification and Localization in RGB Photos
Abstract Routine dental visit is the most common approach to detect the gingivitis. However, such diagnosis can sometimes be unavailable due to the limited medical resources in certain areas and costly for low-income populations. To increase the availability, this study proposes to screen the existence of gingivitis and its irritants, i.e., dental calculus and soft deposits, from oral photos with a novel Multi-Task Learning convolutional neural network (CNN) model. With 625 patients included in this study, the classification Area Under the Curve (AUC) for detecting gingivitis, dental calculus and soft deposits were 87.11%, 80.11%, and 78.57%, respectively; Meanwhile the box-wise localization sensitivity for gingivitis and dental calculus were 66.57% and 45.61%. Moreover, according to a consistency evaluation with three board-certificated dentists, the model achieved a median score of 3.0/5.0 for reasoning locations of soft deposits without any spatial supervision. By comparing to general-purpose CNNs, we showed our model significantly outperformed on both classification and localization tasks, which indicates the effectiveness of Multi-Task Learning on dental disease detection. The results show the potential of deep learning for enabling cost-effective screening of dental diseases among large populations.