Article
Arrhythmic Mitral Valve Prolapse Phenotype: An Unsupervised Machine Learning Analysis Using a Multicenter Cardiac MRI Registry.
Radiology. Cardiothoracic imaging - 1 Jun 2024
Akyea Ralph Kwame, Figliozzi Stefano, Lopes Pedro M, Bauer Klemens B, Moura-Ferreira Sara, Tondi Lara, Mushtaq Saima, Censi Stefano, Pavon Anna Giulia, Bassi Ilaria, Galian-Gay Laura, Teske Arco J, Biondi Federico, Filomena Domenico, Stylianidis Vasileios, Torlasco Camilla, Muraru Denisa, Monney Pierre, Quattrocchi Giuseppina, Maestrini Viviana, Agati Luciano, Monti Lorenzo, Pedrotti Patrizia, Vandenberk Bert, Squeri Angelo, Lombardi Massimo, Ferreira António M, Schwitter Juerg, Aquaro Giovanni Donato, Pontone Gianluca, Chiribiri Amedeo, Rodríguez Palomares José F, Yilmaz Ali, Andreini Daniele, Florian Anca-Rezeda, Francone Marco, Leiner Tim, Abecasis João, Badano Luigi Paolo, Bogaert Jan, Georgiopoulos Georgios, Masci Pier-Giorgio
Abstract excerpt
Purpose To use unsupervised machine learning to identify phenotypic clusters with increased risk of arrhythmic mitral valve prolapse (MVP). Materials and Methods This retrospective study included patients with MVP without hemodynamically significant mitral regurgitation or left ventricular (LV) dysfunction undergoing late gadolinium enhancement (LGE) cardiac MRI between October 2007 and June 2020 in 15 European...
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