Arrhythmic mitral valve prolapse phenotype: An unsupervised machine learning analysis using a multicenter cardiac MRI registry

Ralph Kwame Akyea, Stefano Figliozzi, Pedro M. Lopes, Klemens B. Bauer, Sara Moura-Ferreira, Lara Tondi, Saima Mushtaq, Stefano Censi, Anna Giulia Pavon, Ilaria Bassi, Laura Galian-Gay, Arco J. Teske, Federico Biondi, Domenico Filomena, Vasileios Stylianidis, Camilla Torlasco, Denisa Muraru, Pierre Monney, Giuseppina Quattrocchi, Viviana MaestriniLuciano Agati, Lorenzo Monti, Patrizia Pedrotti, Bert Vandenberk, Angelo Squeri, Massimo Lombardi, Antonio M. Ferreira, Juerg Schwitter, Giovanni Donato Aquaro, Gianluca Pontone, Amedeo Chiribiri, José F.Rodríguez Palomares, Ali Yilmaz, Daniele Andreini, Anca Rezeda Florian, Marco Francone, Tim Leiner, João Abecasis, Luigi Paolo Badano, Jan Bogaert, Georgios Georgiopoulos, Pier Giorgio Masci*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Pharmacology, Toxicology and Pharmaceutical Science