Towards Earlier Detection of Oral Diseases On Smartphones Using Oral and Dental RGB Images

TL;DR

This paper presents a lightweight mobile-compatible machine learning model that detects dental calculus from RGB images, enabling earlier oral disease detection without X-ray reliance, suitable for remote and low-resource settings.

Contribution

It introduces a novel, efficient neural network approach for oral disease detection using RGB images, reducing dependency on X-ray imaging and facilitating mobile health applications.

Findings

Modified MobileNetV3-Small achieved 72.73% accuracy.

ResNet34-based model achieved 81.82% accuracy.

Models demonstrated potential for early detection via mobile devices.

Abstract

Oral diseases such as periodontal (gum) diseases and dental caries (cavities) affect billions of people across the world today. However, previous state-of-the-art models have relied on X-ray images to detect oral diseases, making them inaccessible to remote monitoring, developing countries, and telemedicine. To combat this overuse of X-ray imagery, we propose a lightweight machine learning model capable of detecting calculus (also known as hardened plaque or tartar) in RGB images while running efficiently on low-end devices. The model, a modified MobileNetV3-Small neural network transfer learned from ImageNet, achieved an accuracy of 72.73% (which is comparable to state-of-the-art solutions) while still being able to run on mobile devices due to its reduced memory requirements and processing times. A ResNet34-based model was also constructed and achieved an accuracy of 81.82%. Both of these models were tested on a mobile app, demonstrating their potential to limit the number of serious oral disease cases as their predictions can help patients schedule appointments earlier without the need to go to the clinic.