Fully automated AI for mitral regurgitation severity grading
Original Research | Editorial Comment
Published in JACC: Cardiovascular Imaging, the authors validated a fully automated AI echocardiographic workflow for grading Mitral Regurgitation (MR) severity. The study demonstrated that the machine learning approach was feasible, fast, highly accurate, and predicts 1-year mortality. Its implementation in clinical practice could improve patient care while improving quality and efficiency in echo labs.
Sadeghpour, A., Jiang, Z., Hummel, Y. M., Frost, M., Lam, C. S. P., Shah, S. J., Lund, L. H., Stone, G. W., Swaminathan, M., Weissman, N. J., & Asch, F. M. (2025). An Automated Machine Learning-Based Quantitative Multiparametric Approach for Mitral Regurgitation Severity Grading. JACC. Cardiovascular imaging, 18(1), 1–12. https://doi.org/10.1016/j.jcmg.2024.06.011
Editorial Comment: AI’s Transformative Role in Echocardiographic Evaluation of Mitral Regurgitation
“AI’s integration into echocardiography is not merely an enhancement of existing practices, but a transformation that promises more precise diagnostics and personalized care.”
Xu and Sanchez-Nadales, 2025
Alongside the publication, JACC: Cardiovascular Imaging has featured an Editorial Comment, recognizing the impact of AI-driven automation in echocardiography. The editorial provides expert insights on how AI is reshaping MR severity assessment, enhancing clinical decision-making and improving outcomes for patients.
Xu, B., & Sanchez-Nadales, A. (2025). Artificial Intelligence in Echocardiographic Evaluation of Mitral Regurgitation: Envisioning the Future. JACC. Cardiovascular imaging, 18(1), 13–15. https://doi.org/10.1016/j.jcmg.2024.05.026
