Artificial intelligence-enabled phenotyping of patients with severe aortic stenosis: on the recovery of extra-aortic valve cardiac damage after transcatheter aortic valve replacement

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Title:: Artificial intelligence-enabled phenotyping of patients with severe aortic stenosis: on the recovery of extra-aortic valve cardiac damage after transcatheter aortic valve replacement
Document type:: Zeitschriftenaufsatz
Author(s):: Lachmann, Mark ; Rippen, Elena ; Schuster, Tibor ; Xhepa, Erion ; von Scheidt, Moritz ; Trenkwalder, Teresa ; Pellegrini, Costanza ; Rheude, Tobias ; Hesse, Amelie ; Stundl, Anja ; Harmsen, Gerhard ; Yuasa, Shinsuke ; Schunkert, Heribert ; Kastrati, Adnan ; Laugwitz, Karl-Ludwig ; Joner, Michael ; Kupatt, Christian
Keywords:: Valvular heart disease ; aortic valve stenosis ; transcatheter aortic valve replacement ; echocardiography
Journal title:: Open Heart
Year:: 2022
Journal volume:: 9
Journal issue:: 2
Fulltext / DOI:: doi:10.1136/openhrt-2022-002068
Publisher:: British Cardiovascular Society
E-ISSN:: 2053-3624
Date of publication:: 19.10.2022
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