Arrhythmic Mitral Valve Prolapse Phenotype: An Unsupervised Machine Learning Analysis Using a Multicenter Cardiac MRI Registry
Ralph Kwame Akyea, Stefano Figliozzi, P Lopes, Klemens B. Bauer, Sara 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 Maestrini, Luciano Agati, Lorenzo Monti, Patrizia Pedrotti, Bert Vandenberk, Angelo Squeri, Massimo Lombardi, A Ferreira, Juerg Schwitter, Giovanni Donato Aquaro, Gianluca Pontone, Amedeo Chiribiri, José F. Rodríguez‐Palomares, Ali Yilmaz, Daniele Andreini, Anca Florian, Marco Francone, Tim Leiner, João Abecasis, Luigi P. Badano, Jan Bogaert, Georgios Georgiopoulos, Pier-Giorgio Masci
Abstract
To use unsupervised machine learning to identify phenotypic clusters with increased risk of arrhythmic mitral valve prolapse (MVP).