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Impact of e-ASPECTS software on the performance of physicians compared to a consensus ground truth: a multi-reader, multi-case study
BACKGROUND: The Alberta Stroke Program Early CT Score (ASPECTS) is used to quantify the extent of injury to the brain following acute ischemic stroke (AIS) and to inform treatment decisions. The e-ASPECTS software uses artificial intelligence methods to automatically process non-contrast CT (NCCT) b...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10513025/ https://www.ncbi.nlm.nih.gov/pubmed/37745671 http://dx.doi.org/10.3389/fneur.2023.1221255 |
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author | Kobeissi, Hassan Kallmes, David F. Benson, John Nagelschneider, Alex Madhavan, Ajay Messina, Steven A. Schwartz, Kara Campeau, Norbert Carr, Carrie M. Nasr, Deena M. Braksick, Sherri Scharf, Eugene L. Klaas, James Woodhead, Zoe Victoria Joan Harston, George Briggs, James Joly, Olivier Gerry, Stephen Kuhn, Anna L. Kostas, Angelos A. Nael, Kambiz AbdalKader, Mohamad Kadirvel, Ramanathan Brinjikji, Waleed |
author_facet | Kobeissi, Hassan Kallmes, David F. Benson, John Nagelschneider, Alex Madhavan, Ajay Messina, Steven A. Schwartz, Kara Campeau, Norbert Carr, Carrie M. Nasr, Deena M. Braksick, Sherri Scharf, Eugene L. Klaas, James Woodhead, Zoe Victoria Joan Harston, George Briggs, James Joly, Olivier Gerry, Stephen Kuhn, Anna L. Kostas, Angelos A. Nael, Kambiz AbdalKader, Mohamad Kadirvel, Ramanathan Brinjikji, Waleed |
author_sort | Kobeissi, Hassan |
collection | PubMed |
description | BACKGROUND: The Alberta Stroke Program Early CT Score (ASPECTS) is used to quantify the extent of injury to the brain following acute ischemic stroke (AIS) and to inform treatment decisions. The e-ASPECTS software uses artificial intelligence methods to automatically process non-contrast CT (NCCT) brain scans from patients with AIS affecting the middle cerebral artery (MCA) territory and generate an ASPECTS. This study aimed to evaluate the impact of e-ASPECTS (Brainomix, Oxford, UK) on the performance of US physicians compared to a consensus ground truth. METHODS: The study used a multi-reader, multi-case design. A total of 10 US board-certified physicians (neurologists and neuroradiologists) scored 54 NCCT brain scans of patients with AIS affecting the MCA territory. Each reader scored each scan on two occasions: once with and once without reference to the e-ASPECTS software, in random order. Agreement with a reference standard (expert consensus read with reference to follow-up imaging) was evaluated with and without software support. RESULTS: A comparison of the area under the curve (AUC) for each reader showed a significant improvement from 0.81 to 0.83 (p = 0.028) with the support of the e-ASPECTS tool. The agreement of reader ASPECTS scoring with the reference standard was improved with e-ASPECTS compared to unassisted reading of scans: Cohen's kappa improved from 0.60 to 0.65, and the case-based weighted Kappa improved from 0.70 to 0.81. CONCLUSION: Decision support with the e-ASPECTS software significantly improves the accuracy of ASPECTS scoring, even by expert US neurologists and neuroradiologists. |
format | Online Article Text |
id | pubmed-10513025 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105130252023-09-22 Impact of e-ASPECTS software on the performance of physicians compared to a consensus ground truth: a multi-reader, multi-case study Kobeissi, Hassan Kallmes, David F. Benson, John Nagelschneider, Alex Madhavan, Ajay Messina, Steven A. Schwartz, Kara Campeau, Norbert Carr, Carrie M. Nasr, Deena M. Braksick, Sherri Scharf, Eugene L. Klaas, James Woodhead, Zoe Victoria Joan Harston, George Briggs, James Joly, Olivier Gerry, Stephen Kuhn, Anna L. Kostas, Angelos A. Nael, Kambiz AbdalKader, Mohamad Kadirvel, Ramanathan Brinjikji, Waleed Front Neurol Neurology BACKGROUND: The Alberta Stroke Program Early CT Score (ASPECTS) is used to quantify the extent of injury to the brain following acute ischemic stroke (AIS) and to inform treatment decisions. The e-ASPECTS software uses artificial intelligence methods to automatically process non-contrast CT (NCCT) brain scans from patients with AIS affecting the middle cerebral artery (MCA) territory and generate an ASPECTS. This study aimed to evaluate the impact of e-ASPECTS (Brainomix, Oxford, UK) on the performance of US physicians compared to a consensus ground truth. METHODS: The study used a multi-reader, multi-case design. A total of 10 US board-certified physicians (neurologists and neuroradiologists) scored 54 NCCT brain scans of patients with AIS affecting the MCA territory. Each reader scored each scan on two occasions: once with and once without reference to the e-ASPECTS software, in random order. Agreement with a reference standard (expert consensus read with reference to follow-up imaging) was evaluated with and without software support. RESULTS: A comparison of the area under the curve (AUC) for each reader showed a significant improvement from 0.81 to 0.83 (p = 0.028) with the support of the e-ASPECTS tool. The agreement of reader ASPECTS scoring with the reference standard was improved with e-ASPECTS compared to unassisted reading of scans: Cohen's kappa improved from 0.60 to 0.65, and the case-based weighted Kappa improved from 0.70 to 0.81. CONCLUSION: Decision support with the e-ASPECTS software significantly improves the accuracy of ASPECTS scoring, even by expert US neurologists and neuroradiologists. Frontiers Media S.A. 2023-09-07 /pmc/articles/PMC10513025/ /pubmed/37745671 http://dx.doi.org/10.3389/fneur.2023.1221255 Text en Copyright © 2023 Kobeissi, Kallmes, Benson, Nagelschneider, Madhavan, Messina, Schwartz, Campeau, Carr, Nasr, Braksick, Scharf, Klaas, Woodhead, Harston, Briggs, Joly, Gerry, Kuhn, Kostas, Nael, AbdalKader, Kadirvel and Brinjikji. 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 Kobeissi, Hassan Kallmes, David F. Benson, John Nagelschneider, Alex Madhavan, Ajay Messina, Steven A. Schwartz, Kara Campeau, Norbert Carr, Carrie M. Nasr, Deena M. Braksick, Sherri Scharf, Eugene L. Klaas, James Woodhead, Zoe Victoria Joan Harston, George Briggs, James Joly, Olivier Gerry, Stephen Kuhn, Anna L. Kostas, Angelos A. Nael, Kambiz AbdalKader, Mohamad Kadirvel, Ramanathan Brinjikji, Waleed Impact of e-ASPECTS software on the performance of physicians compared to a consensus ground truth: a multi-reader, multi-case study |
title | Impact of e-ASPECTS software on the performance of physicians compared to a consensus ground truth: a multi-reader, multi-case study |
title_full | Impact of e-ASPECTS software on the performance of physicians compared to a consensus ground truth: a multi-reader, multi-case study |
title_fullStr | Impact of e-ASPECTS software on the performance of physicians compared to a consensus ground truth: a multi-reader, multi-case study |
title_full_unstemmed | Impact of e-ASPECTS software on the performance of physicians compared to a consensus ground truth: a multi-reader, multi-case study |
title_short | Impact of e-ASPECTS software on the performance of physicians compared to a consensus ground truth: a multi-reader, multi-case study |
title_sort | impact of e-aspects software on the performance of physicians compared to a consensus ground truth: a multi-reader, multi-case study |
topic | Neurology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10513025/ https://www.ncbi.nlm.nih.gov/pubmed/37745671 http://dx.doi.org/10.3389/fneur.2023.1221255 |
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