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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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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. |
format | Online Article Text |
id | pubmed-9753125 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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