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Computer-aided imaging analysis in acute ischemic stroke – background and clinical applications

Mokli, Yahia ; Pfaff, Johannes ; Pinto dos Santos, Daniel ; Herweh, Christian ; Nagel, Simon

In: Neurological Research and Practice, 1 (2019), Nr. 23. pp. 1-13. ISSN 2524-3489

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Abstract

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.

Item Type: Article
Journal or Publication Title: Neurological Research and Practice
Volume: 1
Number: 23
Publisher: BioMed Central
Place of Publication: London
Date Deposited: 23 Aug 2019 14:43
Date: 2019
ISSN: 2524-3489
Page Range: pp. 1-13
Faculties / Institutes: Medizinische Fakultät Heidelberg > Neurologische Universitätsklinik
Subjects: 610 Medical sciences Medicine
Uncontrolled Keywords: Acute ischemic stroke, Imaging, Computer aided diagnosis, Artificial intelligence
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