Project Description:

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%.

Project Photo:

Smartphone-enabled remote dizziness triage system

Student Team Members

• Kemar E. Green, DO
• Narayani Wagle
• Jinyan Liu
• John Morkos

Course Faculty

Project Mentors, Sponsors, and Partners

• David S. Zee, MD
• Kirby Gong
• Indranuj Gangan
• Raimond L Winslow, PhD
• Joseph L Greenstein, PhD