Machine Detection of Nystagmus from Video Recordings

Nystagmus is the instability of the eyes reflecting a physiologic change in neural circuitry that connects the inner ear, brain, and the eye. Previous studies have shown that nystagmus precedes MRI changes by 48-72 hours in stroke patients presenting with isolated dizziness or vertigo. Dizziness and vertigo accounts for over 4 million emergency department (ED) visits per year, and It is difficult for ED providers to differentiate between benign and catastrophic nystagmus rapidly and accurately. This increases stroke misdiagnosis rate, stroke-related disabilities, unnecessary hospitalization/testing, and healthcare spending. Using deep learning approaches, we developed a solution that will be able to predict nystagmus from a smartphone video. This will enable more appropriate triage, as well as remote neurologic diagnosis. Our preliminary model had an AUC of 0.87, accuracy of 84.21%, sensitivity of 86.9%, and a specificity of 82.8%.