Empowered Patient Podcast Using Oculometric Technology and Digital Biomarkers to Detect Diagnose and Monitor Progression of Neurological Diseases with Vivian DeWoskin NeuraLight
Vivian DeWoskin, Chief Commercial Officer at NeuraLight, focuses on developing solutions for diagnosing, monitoring and treating neurodegenerative diseases using oculometric data. Using computer vision and deep learning techniques, data from measuring eye position and movement can provide biomarkers for early detection and insights into neurological health. This non-invasive technology is being tested in the PALOMA study, a longitudinal study of Parkinson's patients designed to develop one biomarker as a monitoring and assessment tool.
Vivian explains, "The reason that this works or theoretically works is that there's a very well-characterized ocular motor system. We know a lot about the ocular motor system and the relationship between eye movement and different parts of the brain. When we talk about oculometrics, we're not just measuring one thing. We're talking about measuring multiple aspects of eye movement and function. Those different aspects are related to a variety of different neural pathways. So, in the context of neurodegenerative disease, we can see certain characteristic changes in how a patient's eyes move or function as a direct result of the degeneration happening in particular areas of the brain."
"The distinct signal that we can get from these different types of oculometrics, from different brain regions, gives us a really powerful tool to be able to use for a variety of purposes like we just described. For diagnosing patients early, potentially differentiating between very similar conditions or conditions that appear similar in their outward presentation of symptoms but have a different disease pathology in the brain. We're excited about this methodology for being able to offer us a variety of really exciting tools that ultimately will help us develop better therapeutics for patients and help benefit patients in the long run."
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