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Computer-aided imaging analysis in acute ischemic stroke – background and clinical applications
Tools for medical image analysis have been developed to reduce the time needed to detect abnormalities and to provide more accurate results. Particularly, tools based on artificial intelligence and machine learning techniques have led to significant improvements in medical imaging interpretation in...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7650084/ https://www.ncbi.nlm.nih.gov/pubmed/33324889 http://dx.doi.org/10.1186/s42466-019-0028-y |
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author | Mokli, Yahia Pfaff, Johannes dos Santos, Daniel Pinto Herweh, Christian Nagel, Simon |
author_facet | Mokli, Yahia Pfaff, Johannes dos Santos, Daniel Pinto Herweh, Christian Nagel, Simon |
author_sort | Mokli, Yahia |
collection | PubMed |
description | Tools for medical image analysis have been developed to reduce the time needed to detect abnormalities and to provide more accurate results. Particularly, tools based on artificial intelligence and machine learning techniques have led to significant improvements in medical imaging interpretation in the last decade. Automatic evaluation of acute ischemic stroke in medical imaging is one of the fields that witnessed a major development. Commercially available products so far aim to identify (and quantify) the ischemic core, the ischemic penumbra, the site of arterial occlusion and the collateral flow but they are not (yet) intended as standalone diagnostic tools. Their use can be complementary; they are intended to support physicians’ interpretation of medical images and hence standardise selection of patients for acute treatment. This review provides an introduction into the field of computer-aided diagnosis and focuses on the automatic analysis of non-contrast-enhanced computed tomography, computed tomography angiography and perfusion imaging. Future studies are necessary that allow the evaluation and comparison of different imaging strategies and post-processing algorithms during the diagnosis process in patients with suspected acute ischemic stroke; which may further facilitate the standardisation of treatment and stroke management. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s42466-019-0028-y) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-7650084 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-76500842020-12-14 Computer-aided imaging analysis in acute ischemic stroke – background and clinical applications Mokli, Yahia Pfaff, Johannes dos Santos, Daniel Pinto Herweh, Christian Nagel, Simon Neurol Res Pract Review Tools for medical image analysis have been developed to reduce the time needed to detect abnormalities and to provide more accurate results. Particularly, tools based on artificial intelligence and machine learning techniques have led to significant improvements in medical imaging interpretation in the last decade. Automatic evaluation of acute ischemic stroke in medical imaging is one of the fields that witnessed a major development. Commercially available products so far aim to identify (and quantify) the ischemic core, the ischemic penumbra, the site of arterial occlusion and the collateral flow but they are not (yet) intended as standalone diagnostic tools. Their use can be complementary; they are intended to support physicians’ interpretation of medical images and hence standardise selection of patients for acute treatment. This review provides an introduction into the field of computer-aided diagnosis and focuses on the automatic analysis of non-contrast-enhanced computed tomography, computed tomography angiography and perfusion imaging. Future studies are necessary that allow the evaluation and comparison of different imaging strategies and post-processing algorithms during the diagnosis process in patients with suspected acute ischemic stroke; which may further facilitate the standardisation of treatment and stroke management. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s42466-019-0028-y) contains supplementary material, which is available to authorized users. BioMed Central 2019-08-15 /pmc/articles/PMC7650084/ /pubmed/33324889 http://dx.doi.org/10.1186/s42466-019-0028-y Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Review Mokli, Yahia Pfaff, Johannes dos Santos, Daniel Pinto Herweh, Christian Nagel, Simon Computer-aided imaging analysis in acute ischemic stroke – background and clinical applications |
title | Computer-aided imaging analysis in acute ischemic stroke – background and clinical applications |
title_full | Computer-aided imaging analysis in acute ischemic stroke – background and clinical applications |
title_fullStr | Computer-aided imaging analysis in acute ischemic stroke – background and clinical applications |
title_full_unstemmed | Computer-aided imaging analysis in acute ischemic stroke – background and clinical applications |
title_short | Computer-aided imaging analysis in acute ischemic stroke – background and clinical applications |
title_sort | computer-aided imaging analysis in acute ischemic stroke – background and clinical applications |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7650084/ https://www.ncbi.nlm.nih.gov/pubmed/33324889 http://dx.doi.org/10.1186/s42466-019-0028-y |
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