Background 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. Methods A convolutional neural network was fine-tuned for pupil tracking using > 550 annotated frames: ConVNG. In a cross-sectional approach, slow-phase velocity of optokinetic nystagmus was calculated in 10 subjects using ConVNG and VOG. Equivalence of accuracy and precision was assessed using the “two one-sample t-test” (TOST) and Bayesian interval-null approaches. ConVNG was systematically compared to OpenFace and MediaPipe as computer vision (CV) benchmarks for gaze estimation. Results ConVNG tracking accuracy reached 9–15% of an average pupil diameter. In a fully independent clinical video dataset, ConVNG robustly detected pupil keypoints (median prediction confidence 0.85). SPV measurement accuracy was equivalent to VOG (TOST p < 0.017; Bayes factors (BF) > 24). ConVNG, but not MediaPipe, achieved equivalence to VOG in all SPV calculations. Median precision was 0.30°/s for ConVNG, 0.7°/s for MediaPipe and 0.12°/s for VOG. ConVNG precision was significantly higher than MediaPipe in vertical planes, but both algorithms’ precision was inferior to VOG. Conclusions 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.

Background Eye movement abnormalities are paramount in neurological disorders. However, unaided eye movement assessments lack granularity. Although videooculography (VOG) improves diagnostic accuracy, resource intensiveness preclude its broad use. To bridge this care gap, we here validate a framework for smartphone video-based nystagmography capitalizing on recent computer vision advances. Methods A recurrent convolutional network was fine-tuned for pupil tracking using >550 annotated frames: ConVNG. Slow phase velocity of optokinetic nystagmus was calculated in 10 subjects using both ConVNG and VOG. Equivalence of accuracy and precision was assessed using the two one-sample t-test (TOST) and Bayesian interval-null approaches. Results ConVNG tracking accuracy reached 9-15% of an average pupil diameter. SPV measurement accuracy was equivalent to VOG (p< .017; Bayes factors (BF) > 24). Average precision was 0.30 deg. for ConVNG and 0.12 deg. for VOG. Conclusions ConVNG enables smartphone video nystagmography with an accuracy comparable to VOG and precision approximately one order of magnitude higher than comparable ARKit applications. This serves as a blueprint for highly accessible tools with potential to accelerate progress toward precise and personalized Medicine.