Machine Learning-Based Phenogrouping in Mitral Valve Prolapse Identifies Profiles Associated With Myocardial Fibrosis and Cardiovascular Events.

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

https://www.ncbi.nlm.nih.gov/pubmed/37204382

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;: