Mobile Application for Oral Disease Detection using Federated Learning

TL;DR

This paper presents OralH, a privacy-preserving mobile app using federated learning and YOLOv8 for oral disease detection, enabling self-assessment and health insights without compromising patient data privacy.

Contribution

It introduces a novel federated learning-based mobile application for oral disease detection, combining edge training, YOLOv8, and PWA for accessible, privacy-aware oral health assessment.

Findings

Effective detection of oral health issues using federated YOLOv8 model.

Enhanced user privacy through local data training and federated averaging.

Accessible platform supporting self-assessment and local dental resource guidance.

Abstract

The mouth, often regarded as a window to the internal state of the body, plays an important role in reflecting one's overall health. Poor oral hygiene has far-reaching consequences, contributing to severe conditions like heart disease, cancer, and diabetes, while inadequate care leads to discomfort, pain, and costly treatments. Federated Learning (FL) for object detection can be utilized for this use case due to the sensitivity of the oral image data of the patients. FL ensures data privacy by storing the images used for object detection on the local device and trains the model on the edge. The updated weights are federated to a central server where all the collected weights are updated via The Federated Averaging algorithm. Finally, we have developed a mobile app named OralH which provides user-friendly solutions, allowing people to conduct self-assessments through mouth scans and providing quick oral health insights. Upon detection of the issues, the application alerts the user about potential oral health concerns or diseases and provides details about dental clinics in the user's locality. Designed as a Progressive Web Application (PWA), the platform ensures ubiquitous access, catering to users across devices for a seamless experience. The application aims to provide state-of-the-art segmentation and detection techniques, leveraging the YOLOv8 object detection model to identify oral hygiene issues and diseases. This study deals with the benefits of leveraging FL in healthcare with promising real-world results.