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

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