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Duration and accuracy of automated stroke CT workflow with AI-supported intracranial large vessel occlusion detection

The Automation Platform (AP) is a software platform to support the workflow of radiologists and includes a stroke CT package with integrated artificial intelligence (AI) based tools. The aim of this study was to evaluate the diagnostic performance of the AP for the detection of intracranial large ve...

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Autores principales: Temmen, Sander E., Becks, Marinus J., Schalekamp, Steven, van Leeuwen, Kicky G., Meijer, Frederick J. A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10397283/
https://www.ncbi.nlm.nih.gov/pubmed/37532773
http://dx.doi.org/10.1038/s41598-023-39831-x
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author Temmen, Sander E.
Becks, Marinus J.
Schalekamp, Steven
van Leeuwen, Kicky G.
Meijer, Frederick J. A.
author_facet Temmen, Sander E.
Becks, Marinus J.
Schalekamp, Steven
van Leeuwen, Kicky G.
Meijer, Frederick J. A.
author_sort Temmen, Sander E.
collection PubMed
description The Automation Platform (AP) is a software platform to support the workflow of radiologists and includes a stroke CT package with integrated artificial intelligence (AI) based tools. The aim of this study was to evaluate the diagnostic performance of the AP for the detection of intracranial large vessel occlusions (LVO) on conventional CT angiography (CTA), and the duration of CT processing in a cohort of acute stroke patients. The diagnostic performance for intracranial LVO detection on CTA by the AP was evaluated in a retrospective cohort of 100 acute stroke patients and compared to the diagnostic performance of five radiologists with different levels of experience. The reference standard was set by an independent neuroradiologist, with access to the readings of the different radiologists, clinical data, and follow-up. The data processing time of the AP for ICH detection on non-contrast CT, LVO detection on CTA, and the processing of CTP maps was assessed in a subset 60 patients of the retrospective cohort. This was compared to 13 radiologists, who were prospectively timed for the processing and reading of 21 stroke CTs. The AP showed shorter processing time of CTA (mean 60 versus 395 s) and CTP (mean 196 versus 243–349 s) as compared to radiologists, but showed lower sensitivity for LVO detection (sensitivity 77% of the AP vs mean sensitivity 87% of radiologists). If the AP would have been used as a stand-alone system, 1 ICA occlusion, 2 M1 occlusions and 8 M2 occlusions would have been missed, which would be eligible for mechanical thrombectomy. In conclusion, the AP showed shorter processing time of CTA and CTP as compared with radiologists, which illustrates the potential of the AP to speed-up the diagnostic work-up. However, its performance for LVO detection was lower as compared with radiologists, especially for M2 vessel occlusions.
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spelling pubmed-103972832023-08-04 Duration and accuracy of automated stroke CT workflow with AI-supported intracranial large vessel occlusion detection Temmen, Sander E. Becks, Marinus J. Schalekamp, Steven van Leeuwen, Kicky G. Meijer, Frederick J. A. Sci Rep Article The Automation Platform (AP) is a software platform to support the workflow of radiologists and includes a stroke CT package with integrated artificial intelligence (AI) based tools. The aim of this study was to evaluate the diagnostic performance of the AP for the detection of intracranial large vessel occlusions (LVO) on conventional CT angiography (CTA), and the duration of CT processing in a cohort of acute stroke patients. The diagnostic performance for intracranial LVO detection on CTA by the AP was evaluated in a retrospective cohort of 100 acute stroke patients and compared to the diagnostic performance of five radiologists with different levels of experience. The reference standard was set by an independent neuroradiologist, with access to the readings of the different radiologists, clinical data, and follow-up. The data processing time of the AP for ICH detection on non-contrast CT, LVO detection on CTA, and the processing of CTP maps was assessed in a subset 60 patients of the retrospective cohort. This was compared to 13 radiologists, who were prospectively timed for the processing and reading of 21 stroke CTs. The AP showed shorter processing time of CTA (mean 60 versus 395 s) and CTP (mean 196 versus 243–349 s) as compared to radiologists, but showed lower sensitivity for LVO detection (sensitivity 77% of the AP vs mean sensitivity 87% of radiologists). If the AP would have been used as a stand-alone system, 1 ICA occlusion, 2 M1 occlusions and 8 M2 occlusions would have been missed, which would be eligible for mechanical thrombectomy. In conclusion, the AP showed shorter processing time of CTA and CTP as compared with radiologists, which illustrates the potential of the AP to speed-up the diagnostic work-up. However, its performance for LVO detection was lower as compared with radiologists, especially for M2 vessel occlusions. Nature Publishing Group UK 2023-08-02 /pmc/articles/PMC10397283/ /pubmed/37532773 http://dx.doi.org/10.1038/s41598-023-39831-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Temmen, Sander E.
Becks, Marinus J.
Schalekamp, Steven
van Leeuwen, Kicky G.
Meijer, Frederick J. A.
Duration and accuracy of automated stroke CT workflow with AI-supported intracranial large vessel occlusion detection
title Duration and accuracy of automated stroke CT workflow with AI-supported intracranial large vessel occlusion detection
title_full Duration and accuracy of automated stroke CT workflow with AI-supported intracranial large vessel occlusion detection
title_fullStr Duration and accuracy of automated stroke CT workflow with AI-supported intracranial large vessel occlusion detection
title_full_unstemmed Duration and accuracy of automated stroke CT workflow with AI-supported intracranial large vessel occlusion detection
title_short Duration and accuracy of automated stroke CT workflow with AI-supported intracranial large vessel occlusion detection
title_sort duration and accuracy of automated stroke ct workflow with ai-supported intracranial large vessel occlusion detection
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10397283/
https://www.ncbi.nlm.nih.gov/pubmed/37532773
http://dx.doi.org/10.1038/s41598-023-39831-x
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