Arrhythmic Mitral Valve Prolapse Phenotype: An Unsupervised Machine Learning Analysis Using a Multicenter Cardiac MRI Registry

• Published in: Radiology. Cardiothoracic imaging Vol. 6; no. 3; p. e230247
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: United States 01.06.2024
• Subjects: Adult; Arrhythmias, Cardiac - diagnostic imaging; Arrhythmias, Cardiac - physiopathology; Female; Humans; Magnetic Resonance Imaging; Magnetic Resonance Imaging, Cine - methods; Male; Middle Aged; Mitral Valve Prolapse - diagnostic imaging; Phenotype; Registries; Retrospective Studies; Unsupervised Machine Learning; Unsupervised Machine Learning; Mitral Valve Prolapse; Cardiac; Sudden Cardiac Death; Ventricular Arrhythmia; Cardiac MRI; MR Imaging; Cluster Analysis

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) dysf...