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.
Author: | Stanislas Werfel, Roman Günthner, Alexander Hapfelmeier, Henner Hanssen, Konstantin KotliarORCiD, Uwe Heemann, Christoph Schmaderer |
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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 |
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 |