ArticleJACC. Cardiovascular imaging2026
A Deep Learning Model to Identify Mitral Valve Prolapse From the Echocardiogram.
Article in JACC. Cardiovascular imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Unimodal to multimodal: a systematic review of predictive machine learning models for valvular heart diseases.Frontiers in cardiovascular medicine · 2026Pooled it
- Systolic Anterior Motion After Mitral Valve Repair: Echocardiographic Prediction, Surgical Prevention and Perioperative Management.Journal of clinical medicine · 2026Review
- Multi-view deep learning for echocardiogram interpretation.Nature cardiovascular research · 2026Article
- The Arrhythmogenic Spectrum of Mitral Valve Disease: Pathophysiology, Risk Stratification, and Surgical Management.Journal of clinical medicine · 2026Review
- Cardiovascular remodeling: key mechanisms and clinical implications linking sarcopenia and heart failure.Frontiers in medicine · 2026Review
- Floppy Mitral Valve/Mitral Valve Prolapse: Diagnosis and Clinical Classification in This Continuing Odyssey.European cardiology · 2026Review
- Machine learning and multi-omics technologies for precision cardiovascular medicine: advancing diagnosis, risk prediction, and therapeutic guidance.Frontiers in cardiovascular medicine · 2026Review
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
backgroundMitral valve prolapse (MVP) has a prevalence of 2% to 3% and increases risk of heart failure and sudden death, but diagnosis by transthoracic echocardiography requires time and expertise.
objectivesThis study aims to develop a deep learning model DROID-MVP (Dimensional Reconstruction of Imaging Data-Mitral Valve Prolapse) to classify MVP from digital echocardiogram videos.
methodsDROID-MVP was trained and validated using 1,043,893 echocardiogram videos (48,829 studies) from 16,902 cardiology patients at MGH (Massachusetts General Hospital), and externally validated in 8,888 MGH primary care patients and 257 primary care patients at BWH (Brigham and Women's Hospital). The authors tested associations among DROID-MVP predictions (range: 0-1), mitral regurgitation (MR) severity, and mitral valve repair or replacement (MVR).
resultsOf 16,902 patients (6,391 [38%] women; age 61 ± 16 years) in the derivation sample, 783 (4.6%) had MVP. DROID-MVP accurately identified MVP across the MGH cardiology internal validation set (area under the receiver-operating characteristic curve [AUROC]: 0.947 [95% CI: 0.910-0.984]; average precision [AP]: 0.682 [95% CI: 0.565-0.784]; prevalence: 0.036), MGH primary care external validation set (AUROC: 0.964 [95% CI: 0.951-0.977]; AP: 0.651 [95% CI: 0.578-0.716]; prevalence: 0.022), and BWH primary care external validation set (AUROC: 0.968 [95% CI: 0.946-0.989]; AP: 0.774 [95% CI: 0.666-0.797]; prevalence: 0.113). A high (>0.67) vs low (<0.33) DROID-MVP score was associated with moderate or severe MR (adjusted OR: 2.0 [95% CI: 1.1-3.8]; P = 0.030) and future MVR (adjusted HR: 3.7 [95% CI: 1.5-8.9]; P = 0.004).
conclusionsA deep learning model identifies MVP from echocardiogram videos, and model predictions are associated with clinical endpoints including MR and future MVR. Deep learning can automate MVP diagnosis and potentially generate digital markers of clinically significant MVP.
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