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

Benutzer: Gast

Titel:: Artificial intelligence-enabled phenotyping of patients with severe aortic stenosis: on the recovery of extra-aortic valve cardiac damage after transcatheter aortic valve replacement
Dokumenttyp:: Zeitschriftenaufsatz
Autor(en):: 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
Stichworte:: Valvular heart disease ; aortic valve stenosis ; transcatheter aortic valve replacement ; echocardiography
Zeitschriftentitel:: Open Heart
Jahr:: 2022
Band / Volume:: 9
Heft / Issue:: 2
Volltext / DOI:: doi:10.1136/openhrt-2022-002068
Verlag / Institution:: British Cardiovascular Society
E-ISSN:: 2053-3624
Publikationsdatum:: 19.10.2022
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