@article{ref1,
title="Eye tracking for classification of concussion in adults and pediatrics",
journal="Frontiers in neurology",
year="2022",
author="Samadani, Uzma and Spinner, Robert J. and Dynkowski, Gerard and Kirelik, Susan and Schaaf, Tory and Wall, Stephen P. and Huang, Paul",
volume="13",
number="",
pages="e1039955-e1039955",
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. <br><br>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). <br><br>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%. <br><br>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 &quot;gold-standard&quot; for detection of neurophysiologic disruption due to brain injury.<p /> <p>Language: en</p>",
language="en",
issn="1664-2295",
doi="10.3389/fneur.2022.1039955",
url="http://dx.doi.org/10.3389/fneur.2022.1039955"
}