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Titel:

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

Dokumenttyp:
Journal Article; Multicenter Study; Research Support, Non-U.S. Gov't
Autor(en):
Werfel, Stanislas; Günthner, Roman; Hapfelmeier, Alexander; Hanssen, Henner; Kotliar, Konstantin; Heemann, Uwe; Schmaderer, Christoph
Abstract:
AIMS: 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. METHODS AND RESULTS: We adopted a workflow consisting of noise reduction and extraction of independent components within DVA signals. Predictor...     »
Zeitschriftentitel:
Cardiovasc Res
Jahr:
2022
Band / Volume:
118
Heft / Issue:
2
Seitenangaben Beitrag:
612-621
Volltext / DOI:
doi:10.1093/cvr/cvab040
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/33576412
Print-ISSN:
0008-6363
TUM Einrichtung:
Institut für KI und Informatik in der Medizin; Lehrstuhl für Allgemeinmedizin (Prof. Schneider) (keine SAP-Zuordnung!); Professur für Nephrologie (Prof. Heemann)
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