Florida Atlantic University and collaborators, have developed a novel proof-of-concept deep learning model that leverages real-time data to assist in diagnosing nystagmus, a condition characterized by involuntary, rhythmic eye movements often linked to vestibular or neurological disorders.
The platform allows patients to record their eye movements using a smartphone, securely upload the video to a cloud-based system, and receive remote diagnostic analysis from vestibular and balance experts. At the heart of this innovation is a deep learning framework that uses real-time facial landmark tracking to analyze eye movements. The AI system automatically maps 468 facial landmarks and evaluates slow-phase velocity – a key metric for identifying nystagmus intensity, duration and direction.
Physicians and audiologists can access AI-generated reports via telehealth platforms, compare them with patients' electronic health records, and develop personalized treatment plans. Together, they are working to enhance the model's accuracy, expand testing across diverse patient populations, and move toward FDA approval for broader clinical adoption.
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