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Identification of cardiovascular high-risk groups from dynamic retinal vessel signals using untargeted machine learning

  • Dynamic retinal vessel analysis (DVA) provides a non-invasive way to assess microvascular function in patients and potentially to improve predictions of individual cardiovascular (CV) risk. The aim of our study was to use untargeted machine learning on DVA in order to improve CV mortality prediction and identify corresponding response alterations.

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Author:Stanislas Werfel, Roman Günthner, Alexander Hapfelmeier, Henner Hanssen, Konstantin KotliarORCiD, Uwe Heemann, Christoph Schmaderer
DOI:https://doi.org/10.1093/cvr/cvab040
ISSN:0008-6363
Parent Title (German):Cardiovascular Research
Publisher:Oxford University Press
Place of publication:Oxford
Editor:Tomasz J. Guzik
Document Type:Article
Language:English
Year of Completion:2022
Date of the Publication (Server):2021/12/22
Tag:Haemodialysis; Machine learning; Microcirculation; Myocardial infarction and cardiac death; Retinal vessels
Volume:118
Issue:2
First Page:612
Last Page:621
Link:https://doi.org/10.1093/cvr/cvab040
Zugriffsart:weltweit
Institutes:FH Aachen / Fachbereich Medizintechnik und Technomathematik
FH Aachen / IfB - Institut für Bioengineering
collections:Verlag / Oxford University Press