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

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) wer...

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Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9799333/
https://www.ncbi.nlm.nih.gov/pubmed/36713009
http://dx.doi.org/10.1093/ehjdh/ztac004

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9799333/
https://www.ncbi.nlm.nih.gov/pubmed/36713009
http://dx.doi.org/10.1093/ehjdh/ztac004

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