Background: 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., Objectives: The authors aimed to develop and validate a fully automated machine learning (ML)-based echocardiography workflow for grading MR severity., Methods: 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., Results: 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)., Conclusions: 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., Competing Interests: Funding Support and Author Disclosures Mr Jiang is an employee of Us2.ai. Mr Hummel is an employee of Us2.ai. Mr Frost is an employee of Us2.ai. Dr Lam is supported by a Clinician Scientist Award from the National Medical Research Council of Singapore; has received research support from NovoNordisk and Roche Diagnostics; has served as consultant or on the Advisory Board/Steering Committee/Executive Committee for Alleviant Medical, Allysta Pharma, Amgen, AnaCardio AB, Applied Therapeutics, AstraZeneca, Bayer, Boehringer Ingelheim, Boston Scientific, CardioRenal, Cytokinetics, Darma Inc, EchoNous Inc, Eli Lilly, Impulse Dynamics, Intellia Therapeutics, Ionis Pharmaceutical, Janssen Research and Development LLC, Medscape/WebMD Global LLC, Merck, Novartis, Novo Nordisk, Prosciento Inc, Quidel Corporation, Radcliffe Group Ltd, Recardio Inc, ReCor Medical, Roche Diagnostics, Sanofi, Siemens Healthcare Diagnostics and Us2.ai; has served as cofounder and non-executive director of Us2.ai; has a patent pending (PCT/SG2016/050217; Method for diagnosis and prognosis of chronic heart failure); and holds U.S. Patent No. 10,702,247 for automated clinical workflow that recognizes and analyses 2-dimensional and Doppler echo images for cardiac measurements and the diagnosis, prediction, and prognosis of heart disease. Dr Shah is supported by grants from the U.S. National Institutes of Health (National Heart, Lung, and Blood Institute; U54 HL160273, R01 HL140731, and R01 HL149423), AstraZeneca, Corvia Medical, and Pfizer; and has received consulting fees from Abbott, AstraZeneca, Alleviant, Amgen, Aria CV, Axon Therapies, Bayer, Boehringer-Ingelheim, Boston Scientific, Bristol-Myers Squibb, Cyclerion, Cytokinetics, Edwards Lifesciences, Eidos, Imara, Impulse Dynamics, Intellia, Ionis, Lilly, Merck, Metabolic Flux, MyoKardia, NGM Biopharmaceuticals, Novartis, Novo Nordisk, Pfizer, Prothena, Regeneron, Rivus, Sardocor, Shifamed, Tenax, Tenaya, Ultromics, and United Therapeutics. Dr Lund is supported by Karolinska Institutet, the Swedish Research Council (grant 523-2014-2336), the Swedish Heart Lung Foundation (grants 20150557, 20190310), and the Stockholm County Council (grants 20170112, 20190525); and unrelated to the present work, Dr Lund has received grants, consulting, and honoraria from Abbot, Alleviant, AstraZeneca, Bayer, Biopeutics, Boehringer Ingelheim, Edwards, Merck/Merck Sharp & Dohme, Novartis, Novo Nordisk, Owkin, Pharmacosmos, Vifor Pharma; and has stock ownership in AnaCardio. Dr Stone has received Speaker or other honoraria from Cook, Terumo, QOOL Therapeutics, and Orchestra Biomed; is a consultant to Valfix, TherOx, Vascular Dynamics, Robocath, HeartFlow, Gore, Ablative Solutions, Miracor, Neovasc, V-Wave, Abiomed, Ancora, MAIA Pharmaceuticals, Vectorious, Reva, Matrizyme, Cardiomech; and has equity/options from Ancora, Qool Therapeutics, Cagent, Applied Therapeutics, Biostar family of funds, SpectraWave, Orchestra Biomed, Aria, Cardiac Success, MedFocus family of funds, and Valfix. Dr Swaminathan has received consulting fees from US2.ai. Dr Weissman is the Associate Director of an academic core laboratory with research institutional grants/agreements (MedStar Health) with Us2.ai, Ultromics, TOMTEC, GE, Caption Health, egnite, Abbott, Edwards, Medtronic, Boston Scientific, Corcym, Ancora Heart, Neovasc, InnovHeart, and Polares Medical. Dr Asch is the director of an academic core laboratory with research institutional grants/agreements (MedStar Health) with Us2.ai, Ultromics, TOMTEC, GE, Caption Health, egnite, Abbott, Edwards, Medtronic, Boston Scientific, Corcym, Ancora Heart, Neovasc, InnovHeart, Polares Medical, and Foldax; and is on the Scientific Advisory Board (unpaid) for Us2.ai, Ultromics, and Abbott. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)