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Machine Detection of Nystagmus from Video Recordings

Team Members:
  • Kemar E. Green, DO
  • Narayani Wagle
  • Jinyan Liu
  • John Morkos
  • David S. Zee, MD
  • Kirby Gong
  • Indranuj Gangan
  • Raimond L Winslow, PhD
  • Joseph L Greenstein, PhD


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%.


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