Tracking Dynamic Facial Function in Facial Palsy Using Machine Learning Algorithms

This research introduces a machine learning-based approach for assessing dynamic facial function in facial palsy patients through objective iris movement tracking. Integrated with a remote monitoring application, this method aims to improve diagnostic accuracy and optimize outcomes in enhancing patient quality of life. The proposed system leverages real-time video analysis to capture subtle iris and facial muscle movements, which are essential indicators of facial nerve health. By offering remote accessibility, it facilitates early detection and personalized treatment recommendations, bridging gaps in care for patients with limited access to specialized facilities. This approach has the potential to advance clinical practices by providing a standardized, non-invasive, and efficient tool for monitoring and evaluating facial palsy recovery.