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Eye movement abnormalities are commonplace in neurological disorders. However, unaided eye movement assessments lack granularity. Although videooculography VOG improves diagnostic accuracy, resource intensiveness precludes its broad use. To bridge this care gap, we here validate a framework for smartphone video-based nystagmography capitalizing on recent computer vision advances.
In a fully independent clinical video dataset, ConVNG robustly detected pupil keypoints median prediction confidence 0. Median precision was 0. ConVNG enables offline smartphone video nystagmography with an accuracy comparable to VOG and significantly higher precision than MediaPipe, a benchmark computer vision application for gaze estimation. This serves as a blueprint for highly accessible tools with potential to accelerate progress toward precise and personalized Medicine.
Disturbances of ocular motility and coordination mark a highly interdisciplinary subject matter extending into the fields of neurology, ophthalmology and otology. Somewhat unique to motor physiology, eye movements can be grouped into a limited set of functional domains which specifically map onto distinct or partly overlapping neural circuits [ 1 ].
These involve vestibular and visual afferents as well as neuronal networks encompassing the vast extent of cortex, brainstem and cerebellum. This is especially true in the context of vertigo and dizziness, which are among the most prevalent and economically challenging conditions world-wide [ 3 , 4 ]. Over the last century, different approaches to register eye movements and in particular nystagmus have been developed, ranging from early eye photography to invasive i.
