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Hyperdense Artery Sign in Patients With Acute Ischemic Stroke–Automated Detection With Artificial Intelligence-Driven Software
BACKGROUND: Hyperdense artery sign (HAS) on non-contrast CT (NCCT) can indicate a large vessel occlusion (LVO) in patients with acute ischemic stroke. HAS detection belongs to routine reporting in patients with acute stroke and can help to identify patients in whom LVO is not initially suspected. We...
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/PMC9016329/ https://www.ncbi.nlm.nih.gov/pubmed/35449516 http://dx.doi.org/10.3389/fneur.2022.807145 |
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author | Weyland, Charlotte Sabine Papanagiotou, Panagiotis Schmitt, Niclas Joly, Olivier Bellot, Pau Mokli, Yahia Ringleb, Peter Arthur Kastrup, A. Möhlenbruch, Markus A. Bendszus, Martin Nagel, Simon Herweh, Christian |
author_facet | Weyland, Charlotte Sabine Papanagiotou, Panagiotis Schmitt, Niclas Joly, Olivier Bellot, Pau Mokli, Yahia Ringleb, Peter Arthur Kastrup, A. Möhlenbruch, Markus A. Bendszus, Martin Nagel, Simon Herweh, Christian |
author_sort | Weyland, Charlotte Sabine |
collection | PubMed |
description | BACKGROUND: Hyperdense artery sign (HAS) on non-contrast CT (NCCT) can indicate a large vessel occlusion (LVO) in patients with acute ischemic stroke. HAS detection belongs to routine reporting in patients with acute stroke and can help to identify patients in whom LVO is not initially suspected. We sought to evaluate automated HAS detection by commercial software and compared its performance to that of trained physicians against a reference standard. METHODS: Non-contrast CT scans from 154 patients with and without LVO proven by CT angiography (CTA) were independently rated for HAS by two blinded neuroradiologists and an AI-driven algorithm (Brainomix®). Sensitivity and specificity were analyzed for the clinicians and the software. As a secondary analysis, the clot length was automatically calculated by the software and compared with the length manually outlined on CTA images as the reference standard. RESULTS: Among 154 patients, 84 (54.5%) had CTA-proven LVO. HAS on the correct side was detected with a sensitivity and specificity of 0.77 (CI:0.66–0.85) and 0.87 (0.77–0.94), 0.8 (0.69–0.88) and 0.97 (0.89–0.99), and 0.93 (0.84–0.97) and 0.71 (0.59–0.81) by the software and readers 1 and 2, respectively. The automated estimation of the thrombus length was in moderate agreement with the CTA-based reference standard [intraclass correlation coefficient (ICC) 0.73]. CONCLUSION: Automated detection of HAS and estimation of thrombus length on NCCT by the tested software is feasible with a sensitivity and specificity comparable to that of trained neuroradiologists. |
format | Online Article Text |
id | pubmed-9016329 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90163292022-04-20 Hyperdense Artery Sign in Patients With Acute Ischemic Stroke–Automated Detection With Artificial Intelligence-Driven Software Weyland, Charlotte Sabine Papanagiotou, Panagiotis Schmitt, Niclas Joly, Olivier Bellot, Pau Mokli, Yahia Ringleb, Peter Arthur Kastrup, A. Möhlenbruch, Markus A. Bendszus, Martin Nagel, Simon Herweh, Christian Front Neurol Neurology BACKGROUND: Hyperdense artery sign (HAS) on non-contrast CT (NCCT) can indicate a large vessel occlusion (LVO) in patients with acute ischemic stroke. HAS detection belongs to routine reporting in patients with acute stroke and can help to identify patients in whom LVO is not initially suspected. We sought to evaluate automated HAS detection by commercial software and compared its performance to that of trained physicians against a reference standard. METHODS: Non-contrast CT scans from 154 patients with and without LVO proven by CT angiography (CTA) were independently rated for HAS by two blinded neuroradiologists and an AI-driven algorithm (Brainomix®). Sensitivity and specificity were analyzed for the clinicians and the software. As a secondary analysis, the clot length was automatically calculated by the software and compared with the length manually outlined on CTA images as the reference standard. RESULTS: Among 154 patients, 84 (54.5%) had CTA-proven LVO. HAS on the correct side was detected with a sensitivity and specificity of 0.77 (CI:0.66–0.85) and 0.87 (0.77–0.94), 0.8 (0.69–0.88) and 0.97 (0.89–0.99), and 0.93 (0.84–0.97) and 0.71 (0.59–0.81) by the software and readers 1 and 2, respectively. The automated estimation of the thrombus length was in moderate agreement with the CTA-based reference standard [intraclass correlation coefficient (ICC) 0.73]. CONCLUSION: Automated detection of HAS and estimation of thrombus length on NCCT by the tested software is feasible with a sensitivity and specificity comparable to that of trained neuroradiologists. Frontiers Media S.A. 2022-04-05 /pmc/articles/PMC9016329/ /pubmed/35449516 http://dx.doi.org/10.3389/fneur.2022.807145 Text en Copyright © 2022 Weyland, Papanagiotou, Schmitt, Joly, Bellot, Mokli, Ringleb, Kastrup, Möhlenbruch, Bendszus, Nagel and Herweh. 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 Weyland, Charlotte Sabine Papanagiotou, Panagiotis Schmitt, Niclas Joly, Olivier Bellot, Pau Mokli, Yahia Ringleb, Peter Arthur Kastrup, A. Möhlenbruch, Markus A. Bendszus, Martin Nagel, Simon Herweh, Christian Hyperdense Artery Sign in Patients With Acute Ischemic Stroke–Automated Detection With Artificial Intelligence-Driven Software |
title | Hyperdense Artery Sign in Patients With Acute Ischemic Stroke–Automated Detection With Artificial Intelligence-Driven Software |
title_full | Hyperdense Artery Sign in Patients With Acute Ischemic Stroke–Automated Detection With Artificial Intelligence-Driven Software |
title_fullStr | Hyperdense Artery Sign in Patients With Acute Ischemic Stroke–Automated Detection With Artificial Intelligence-Driven Software |
title_full_unstemmed | Hyperdense Artery Sign in Patients With Acute Ischemic Stroke–Automated Detection With Artificial Intelligence-Driven Software |
title_short | Hyperdense Artery Sign in Patients With Acute Ischemic Stroke–Automated Detection With Artificial Intelligence-Driven Software |
title_sort | hyperdense artery sign in patients with acute ischemic stroke–automated detection with artificial intelligence-driven software |
topic | Neurology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9016329/ https://www.ncbi.nlm.nih.gov/pubmed/35449516 http://dx.doi.org/10.3389/fneur.2022.807145 |
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