A deep learning approach to automatic gingivitis screening based on classification and localization in RGB photos

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. 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. The study can be meaningful for promoting the public dental health, since it sheds light on a cost-effective and ubiquitous solution for the early detection of dental issues. 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, according to our experiments, the model can also localize the three types of findings on oral photos with moderate accuracy, which enables the model to explain the screen results. 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. In all, the study shows the potential of deep learning for enabling the screening of dental diseases among large populations.

Automated summary

This study proposes a novel Multi-Task Learning CNN model to screen gingivitis, dental calculus, and soft deposits from oral photos, providing a cost-effective and ubiquitous solution for improving dental health. The model achieved high classification AUC for detecting these issues and moderate accuracy for localizing them, surpassing general-purpose CNN models and demonstrating the effectiveness of Multi-Task Learning in dental disease detection. Overall, the study highlights the potential of deep learning in enabling the screening of dental diseases in large populations.