  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.
