Machine Learning-Based Phenogrouping in Mitral Valve Prolapse Identifies Profiles Associated With Myocardial Fibrosis and Cardiovascular Events.
Fiche publication
Date publication
mai 2023
Journal
JACC. Cardiovascular imaging
Auteurs
Membres identifiés du Cancéropôle Est :
Pr MARIE Pierre-Yves, Dr BEAUMONT Marine
Tous les auteurs :
Huttin O, Girerd N, Jobbe-Duval A, Constant Dit Beaufils AL, Senage T, Filippetti L, Cueff C, Duarte K, Fraix A, Piriou N, Mandry D, Pace N, Le Scouarnec S, Capoulade R, Echivard M, Sellal JM, Marrec M, Beaumont M, Hossu G, Trochu JN, Sadoul N, Marie PY, Guenancia C, Schott JJ, Roussel JC, Serfaty JM, Selton-Suty C, Le Tourneau T
Lien Pubmed
Résumé
Structural changes and myocardial fibrosis quantification by cardiac imaging have become increasingly important to predict cardiovascular events in patients with mitral valve prolapse (MVP). In this setting, it is likely that an unsupervised approach using machine learning may improve their risk assessment.
Mots clés
cardiac magnetic resonance, echocardiography, machine learning, mitral regurgitation, mitral valve prolapse, myocardial fibrosis, prognosis value
Référence
JACC Cardiovasc Imaging. 2023 05 13;: