An Automated Machine Learning–Based Quantitative Multiparametric Approach for Mitral Regurgitation Severity Grading

• Published in: JACC. Cardiovascular imaging Vol. 18; no. 1; pp. 1 - 12
• Main Authors: 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.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.01.2025
• Subjects: 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
• ISSN: 1936-878X; 1876-7591; 1876-7591
• DOI: 10.1016/j.jcmg.2024.06.011

Considering the high prevalence of mitral regurgitation (MR) and the highly subjective, variable MR severity reporting, an automated tool that could screen patients for clinically significant MR (≥ moderate) would streamline the diagnostic/therapeutic pathways and ultimately improve patient outcomes. The authors aimed to develop and validate a fully automated machine learning (ML)–based echocardiography workflow for grading MR severity. ML algorithms were trained on echocardiograms from 2 observational cohorts and validated in patients from 2 additional independent studies. Multiparametric echocardiography core laboratory MR assessment served as ground truth. The machine was trained to measure 16 MR-related parameters. Multiple ML models were developed to find the optimal parameters and preferred ML model for MR severity grading. The preferred ML model used 9 parameters. Image analysis was feasible in 99.3% of cases and took 80 ± 5 seconds per case. The accuracy for grading MR severity (none to severe) was 0.80, and for significant (moderate or severe) vs nonsignificant MR was 0.97 with a sensitivity of 0.96 and specificity of 0.98. The model performed similarly in cases of eccentric and central MR. Patients graded as having severe MR had higher 1-year mortality (adjusted HR: 5.20 [95% CI: 1.24-21.9]; P = 0.025 compared with mild). An automated multiparametric ML model for grading MR severity is feasible, fast, highly accurate, and predicts 1-year mortality. Its implementation in clinical practice could improve patient care by facilitating referral to specialized clinics and access to evidence-based therapies while improving quality and efficiency in the echocardiography laboratory. [Display omitted]