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Eye tracking for classification of concussion in adults and pediatrics

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...

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Autores principales: Samadani, Uzma, Spinner, Robert J., Dynkowski, Gerard, Kirelik, Susan, Schaaf, Tory, Wall, Stephen P., Huang, Paul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9753125/
https://www.ncbi.nlm.nih.gov/pubmed/36530640
http://dx.doi.org/10.3389/fneur.2022.1039955
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author Samadani, Uzma
Spinner, Robert J.
Dynkowski, Gerard
Kirelik, Susan
Schaaf, Tory
Wall, Stephen P.
Huang, Paul
author_facet Samadani, Uzma
Spinner, Robert J.
Dynkowski, Gerard
Kirelik, Susan
Schaaf, Tory
Wall, Stephen P.
Huang, Paul
author_sort Samadani, Uzma
collection PubMed
description 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.
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spelling pubmed-97531252022-12-16 Eye tracking for classification of concussion in adults and pediatrics Samadani, Uzma Spinner, Robert J. Dynkowski, Gerard Kirelik, Susan Schaaf, Tory Wall, Stephen P. Huang, Paul Front Neurol Neurology 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. Frontiers Media S.A. 2022-12-01 /pmc/articles/PMC9753125/ /pubmed/36530640 http://dx.doi.org/10.3389/fneur.2022.1039955 Text en Copyright © 2022 Samadani, Spinner, Dynkowski, Kirelik, Schaaf, Wall and Huang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neurology
Samadani, Uzma
Spinner, Robert J.
Dynkowski, Gerard
Kirelik, Susan
Schaaf, Tory
Wall, Stephen P.
Huang, Paul
Eye tracking for classification of concussion in adults and pediatrics
title Eye tracking for classification of concussion in adults and pediatrics
title_full Eye tracking for classification of concussion in adults and pediatrics
title_fullStr Eye tracking for classification of concussion in adults and pediatrics
title_full_unstemmed Eye tracking for classification of concussion in adults and pediatrics
title_short Eye tracking for classification of concussion in adults and pediatrics
title_sort eye tracking for classification of concussion in adults and pediatrics
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9753125/
https://www.ncbi.nlm.nih.gov/pubmed/36530640
http://dx.doi.org/10.3389/fneur.2022.1039955
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