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 systemat

SUBMITTER: Friedrich MU 

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

REPOSITORIES: biostudies-literature