Dataset Information

Smartphone video nystagmography using convolutional neural networks: ConVNG.

ABSTRACT:

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.

SUBMITTER: Friedrich MU

PROVIDER: S-EPMC10129923 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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Publications

Smartphone video nystagmography using convolutional neural networks: ConVNG.

Friedrich Maximilian U MU Schneider Erich E Buerklein Miriam M Taeger Johannes J Hartig Johannes J Volkmann Jens J Peach Robert R Zeller Daniel D

Journal of neurology 20221123 5

<h4>Background</h4>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.<h4>Methods</h4>A convolutional neural network was fine-tuned for pupil tracking using > 550 anno ...[more]

PMID: 36422668

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Dataset Information

Smartphone video nystagmography using convolutional neural networks: ConVNG.

Background

Methods

Results

Conclusions

Publications

Smartphone video nystagmography using convolutional neural networks: ConVNG.

Similar Datasets

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