Evaluation of Dental Image Augmentation for the Severity Assessment of Periodontal Disease

Abstract

By exploring the feasibility of medical imaging applicable to periodontal disease, we have designed a MapReduce-like deep learning model for the severity assessment by estimating the pocket depth from oral images. However, deep learning typically relies on supervised training with a large annotated dataset, and medical data often faces an insufficiency in quantity and variety. Furthermore, obtaining patient data and annotating such data by experts still remain a challenge. To overcome the insufficiency in the data, we propose random cropping and GAN-based augmentation methods on tooth pocket region images extracted from oral images. We verify that the proposed methods successfully increase the number of training data and its variety, and these synthetic data contribute to improving the estimation accuracy from 78.3% to 84.5%, and sensitivity from 50.4% to 74.0%, with specificity of around 90%, compared to the MapReduce-like model without the augmentation.