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Journal Article

Citation

Samadani U, Spinner RJ, Dynkowski G, Kirelik S, Schaaf T, Wall SP, Huang P. Front. Neurol. 2022; 13: e1039955.

Copyright

(Copyright © 2022, Frontiers Research Foundation)

DOI

10.3389/fneur.2022.1039955

PMID

36530640

PMCID

PMC9753125

Abstract

INTRODUCTION: In order to obtain FDA Marketing Authorization for aid in the diagnosis of concussion, an eye tracking study in an intended use population was conducted.

METHODS: Potentially concussed subjects recruited in emergency department and concussion clinic settings prospectively underwent eye tracking and a subset of the Sport Concussion Assessment Tool 3 at 6 sites. The results of an eye tracking-based classifier model were then validated against a pre-specified algorithm with a cutoff for concussed vs. non-concussed. The sensitivity and specificity of eye tracking were calculated after plotting of the receiver operating characteristic curve and calculation of the AUC (area under curve).

RESULTS: When concussion is defined by SCAT3 subsets, the sensitivity and specificity of an eye tracking algorithm was 80.4 and 66.1%, The AUC was 0.718. The misclassification rate (n = 282) was 31.6%.

CONCLUSION: A pre-specified algorithm and cutoff for diagnosis of concussion vs. non-concussion has a sensitivity and specificity that is useful as a baseline-free aid in diagnosis of concussion. Eye tracking has potential to serve as an objective "gold-standard" for detection of neurophysiologic disruption due to brain injury.


Language: en

Keywords

concussion; eye tracking; baseline; baseline concussion; eye movement tracking

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