Article | Feb 19, 2025
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.
Editorial Comment: AI’s Transformative Role in Echocardiographic Evaluation of Mitral Regurgitation
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.
Read the full Editorial Comment here →
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
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