Quantifying Induced Nystagmus Using a Smartphone Eye Tracking Application (EyePhone).

Area of Science:

• Ophthalmology
• Neurology
• Medical Technology

Background:

• Millions of annual emergency department visits for dizziness in the US.
• Vestibular strokes account for a significant portion of these visits.
• Current diagnostic methods for vestibular stroke rely on subtle eye movements, posing a challenge for emergency physicians.

Purpose of the Study:

• To evaluate the accuracy of EyePhone, a smartphone eye-tracking application, in quantifying nystagmus.
• To assess the potential of EyePhone as a tool for diagnosing vestibular strokes in emergency settings.

Main Methods:

• Prospective enrollment of healthy volunteers.
• Recording induced nystagmus using EyePhone and comparing with video oculography (VOG).
• Analysis of slow phase velocities and receiver operating characteristic curves for nystagmus detection.

Main Results:

• High correlation between EyePhone and VOG recordings for slow phase velocities (r=0.98 horizontal, r=0.94 vertical).
• Calibration improved linear regression slopes for eye movement quantification.
• EyePhone demonstrated an area under the curve of 0.87 for detecting horizontal and vertical nystagmus.

Conclusions:

• EyePhone accurately detects and quantifies optokinetic nystagmus.
• The smartphone application shows comparable performance to traditional VOG goggles.
• EyePhone has the potential to overcome barriers in diagnosing vestibular strokes in emergency departments.