An Automated Machine Learning–Based Quantitative Multiparametric Approach for Mitral Regurgitation Severity Grading - Sadeghpour, Anita; Jiang, Zhubo; Hummel, Yoran M.; Frost, Matthew; Lam, Carolyn S.P.; Shah, Sanjiv J.; Lund, Lars H.; Stone, Gregg W.; Swaminathan, Madhav; Weissman, Neil J.; Asch, Federico M. (JACC. Cardiovascular imaging, 2025)
Keywords: Aged; artificial intelligence; Automation; continuous wave Doppler density; echocardiography; Feasibility Studies; Female; Humans; Image Interpretation, Computer-Assisted; Machine Learning; Male; Middle Aged; mitral regurgitation; Mitral Valve - diagnostic imaging; Mitral Valve - physiopathology; Mitral Valve Insufficiency - diagnostic imaging; Mitral Valve Insufficiency - mortality; Mitral Valve Insufficiency - physiopathology; Predictive Value of Tests; Prognosis; Reproducibility of Results; Severity of Illness Index; Workflow; RAR; TTE; mitral regurgitation; MR; echocardiography; LVESV; LV; CWDD; PASP; machine learning; LVOT; ROI; VC; artificial intelligence; A4C; A2C; continuous wave Doppler density; LA; PLAX; LVEDV; ML