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External Validation of e‐ASPECTS Software for Interpreting Brain CT in Stroke
OBJECTIVE: The purpose of this study was to test e‐ASPECTS software in patients with stroke. Marketed as a decision‐support tool, e‐ASPECTS may detect features of ischemia or hemorrhage on computed tomography (CT) imaging and quantify ischemic extent using Alberta Stroke Program Early CT Score (ASPE...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley & Sons, Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9826303/ https://www.ncbi.nlm.nih.gov/pubmed/36053916 http://dx.doi.org/10.1002/ana.26495 |
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author | Mair, Grant White, Philip Bath, Philip M. Muir, Keith W. Al‐Shahi Salman, Rustam Martin, Chloe Dye, David Chappell, Francesca M. Vacek, Adam von Kummer, Rüdiger Macleod, Malcolm Sprigg, Nikola Wardlaw, Joanna M. |
author_facet | Mair, Grant White, Philip Bath, Philip M. Muir, Keith W. Al‐Shahi Salman, Rustam Martin, Chloe Dye, David Chappell, Francesca M. Vacek, Adam von Kummer, Rüdiger Macleod, Malcolm Sprigg, Nikola Wardlaw, Joanna M. |
author_sort | Mair, Grant |
collection | PubMed |
description | OBJECTIVE: The purpose of this study was to test e‐ASPECTS software in patients with stroke. Marketed as a decision‐support tool, e‐ASPECTS may detect features of ischemia or hemorrhage on computed tomography (CT) imaging and quantify ischemic extent using Alberta Stroke Program Early CT Score (ASPECTS). METHODS: Using CT from 9 stroke studies, we compared software with masked experts. As per indications for software use, we assessed e‐ASPECTS results for patients with/without middle cerebral artery (MCA) ischemia but no other cause of stroke. In an analysis outside the intended use of the software, we enriched our dataset with non‐MCA ischemia, hemorrhage, and mimics to simulate a representative “front door” hospital population. With final diagnosis as the reference standard, we tested the diagnostic accuracy of e‐ASPECTS for identifying stroke features (ischemia, hyperattenuated arteries, and hemorrhage) in the representative population. RESULTS: We included 4,100 patients (51% women, median age = 78 years, National Institutes of Health Stroke Scale [NIHSS] = 10, onset to scan = 2.5 hours). Final diagnosis was ischemia (78%), hemorrhage (14%), or mimic (8%). From 3,035 CTs with expert‐rated ASPECTS, most (2084/3035, 69%) e‐ASPECTS results were within one point of experts. In the representative population, the diagnostic accuracy of e‐ASPECTS was 71% (95% confidence interval [CI] = 70–72%) for detecting ischemic features, 85% (83–86%) for hemorrhage. Software identified more false positive ischemia (12% vs 2%) and hemorrhage (14% vs <1%) than experts. INTERPRETATION: On independent testing, e‐ASPECTS provided moderate agreement with experts and overcalled stroke features. Therefore, future prospective trials testing impacts of artificial intelligence (AI) software on patient care and outcome are required before widespread implementation of stroke decision‐support software. ANN NEUROL 2022;92:943–957 |
format | Online Article Text |
id | pubmed-9826303 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley & Sons, Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98263032023-01-09 External Validation of e‐ASPECTS Software for Interpreting Brain CT in Stroke Mair, Grant White, Philip Bath, Philip M. Muir, Keith W. Al‐Shahi Salman, Rustam Martin, Chloe Dye, David Chappell, Francesca M. Vacek, Adam von Kummer, Rüdiger Macleod, Malcolm Sprigg, Nikola Wardlaw, Joanna M. Ann Neurol Research Articles OBJECTIVE: The purpose of this study was to test e‐ASPECTS software in patients with stroke. Marketed as a decision‐support tool, e‐ASPECTS may detect features of ischemia or hemorrhage on computed tomography (CT) imaging and quantify ischemic extent using Alberta Stroke Program Early CT Score (ASPECTS). METHODS: Using CT from 9 stroke studies, we compared software with masked experts. As per indications for software use, we assessed e‐ASPECTS results for patients with/without middle cerebral artery (MCA) ischemia but no other cause of stroke. In an analysis outside the intended use of the software, we enriched our dataset with non‐MCA ischemia, hemorrhage, and mimics to simulate a representative “front door” hospital population. With final diagnosis as the reference standard, we tested the diagnostic accuracy of e‐ASPECTS for identifying stroke features (ischemia, hyperattenuated arteries, and hemorrhage) in the representative population. RESULTS: We included 4,100 patients (51% women, median age = 78 years, National Institutes of Health Stroke Scale [NIHSS] = 10, onset to scan = 2.5 hours). Final diagnosis was ischemia (78%), hemorrhage (14%), or mimic (8%). From 3,035 CTs with expert‐rated ASPECTS, most (2084/3035, 69%) e‐ASPECTS results were within one point of experts. In the representative population, the diagnostic accuracy of e‐ASPECTS was 71% (95% confidence interval [CI] = 70–72%) for detecting ischemic features, 85% (83–86%) for hemorrhage. Software identified more false positive ischemia (12% vs 2%) and hemorrhage (14% vs <1%) than experts. INTERPRETATION: On independent testing, e‐ASPECTS provided moderate agreement with experts and overcalled stroke features. Therefore, future prospective trials testing impacts of artificial intelligence (AI) software on patient care and outcome are required before widespread implementation of stroke decision‐support software. ANN NEUROL 2022;92:943–957 John Wiley & Sons, Inc. 2022-09-23 2022-12 /pmc/articles/PMC9826303/ /pubmed/36053916 http://dx.doi.org/10.1002/ana.26495 Text en © 2022 The Authors. Annals of Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles Mair, Grant White, Philip Bath, Philip M. Muir, Keith W. Al‐Shahi Salman, Rustam Martin, Chloe Dye, David Chappell, Francesca M. Vacek, Adam von Kummer, Rüdiger Macleod, Malcolm Sprigg, Nikola Wardlaw, Joanna M. External Validation of e‐ASPECTS Software for Interpreting Brain CT in Stroke |
title | External Validation of e‐ASPECTS Software for Interpreting Brain CT in Stroke |
title_full | External Validation of e‐ASPECTS Software for Interpreting Brain CT in Stroke |
title_fullStr | External Validation of e‐ASPECTS Software for Interpreting Brain CT in Stroke |
title_full_unstemmed | External Validation of e‐ASPECTS Software for Interpreting Brain CT in Stroke |
title_short | External Validation of e‐ASPECTS Software for Interpreting Brain CT in Stroke |
title_sort | external validation of e‐aspects software for interpreting brain ct in stroke |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9826303/ https://www.ncbi.nlm.nih.gov/pubmed/36053916 http://dx.doi.org/10.1002/ana.26495 |
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