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Title:

Harnessing feature extraction capacities from a pre-trained convolutional neural network (VGG-16) for the unsupervised distinction of aortic outflow velocity profiles in patients with severe aortic stenosis.

Document type:
Journal Article
Author(s):
Lachmann, Mark; Rippen, Elena; Rueckert, Daniel; Schuster, Tibor; Xhepa, Erion; von Scheidt, Moritz; Pellegrini, Costanza; Trenkwalder, Teresa; Rheude, Tobias; Stundl, Anja; Thalmann, Ruth; Harmsen, Gerhard; Yuasa, Shinsuke; Schunkert, Heribert; Kastrati, Adnan; Joner, Michael; Kupatt, Christian; Laugwitz, Karl Ludwig
Abstract:
AIMS: Hypothesizing that aortic outflow velocity profiles contain more valuable information about aortic valve obstruction and left ventricular contractility than can be captured by the human eye, features of the complex geometry of Doppler tracings from patients with severe aortic stenosis (AS) were extracted by a convolutional neural network (CNN). METHODS AND RESULTS: After pre-training a CNN (VGG-16) on a large data set (ImageNet data set; 14 million images belonging to 1000 classes), the c...     »
Journal title abbreviation:
Eur Heart J Digit Health
Year:
2022
Journal volume:
3
Journal issue:
2
Pages contribution:
153-168
Fulltext / DOI:
doi:10.1093/ehjdh/ztac004
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/36713009
TUM Institution:
Institut für KI und Informatik in der Medizin; Klinik für Herz- und Kreislauferkrankungen im Erwachsenenalter (Prof. Schunkert); Klinik und Poliklinik für Innere Medizin I, Kardiologie
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