ArticleJournal of the American College of Cardiology2023
Deep Learning-Enabled Assessment of Left Heart Structure and Function Predicts Cardiovascular Outcomes.
Article in Journal of the American College of Cardiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
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Who cites it
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning in Left Ventricular Hypertrophy Detection: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Deep learning-enabled assessment of right ventricular size and function is associated with cardiovascular outcomes.Nature cardiovascular research · 2026Article
- Early Prediction of Cardiomyopathy in Pediatric Cancer Survivors Using Echocardiographic Video-Based Deep Learning.Echocardiography (Mount Kisco, N.Y.) · 2026Article
- Predictive Effect of Echocardiographic Assessment of Myocardial Efficiency on Martial Arts Performance.Journal of sports science & medicine · 2026Observational
- ECG Signatures and Long-Term Ischemic Stroke Risk: A Deep Learning Analysis of 200,000 Patients.Journal of the American College of Cardiology · 2026Article
- A view-flexible deep learning framework for automated analysis of 2D echocardiography.NPJ cardiovascular health · 2026Article
- Precision cardiovascular medicine with big data and AI.NPJ digital medicine · 2026Review
- Artificial Intelligence in Cardiovascular Imaging: From Automated Acquisition to Precision Diagnostics and Clinical Decision Support.Medical sciences (Basel, Switzerland) · 2026Review
- Multimodal multi-instance learning for cardiopulmonary exercise testing performance prediction.NPJ digital medicine · 2026Article
- Associations of Integrated Left Ventricular Myocardial Strain Metrics and Incident Atrial Fibrillation.CJC open · 2026Article
- Multiview deep learning improves detection of major cardiac conditions from echocardiography.Nature cardiovascular research · 2026Article
- Multi-view deep learning for echocardiogram interpretation.Nature cardiovascular research · 2026Article
- Artificial intelligence-enhanced echocardiography in cardiovascular disease management.Nature reviews. Cardiology · 2026Review
- AI-assisted multimodal assessment for right ventricular function from echocardiography predicts mortality in patients with pulmonary hypertension and right heart failure.Scientific reports · 2026Article
- Optimizing imaging techniques in hypertrophic cardiomyopathy: challenges and opportunities.European heart journal. Imaging methods and practice · 2026Review
- A Deep Learning Model to Identify Mitral Valve Prolapse From the Echocardiogram.JACC. Cardiovascular imaging · 2026Article
- Artificial Intelligence Tools in Myocardial Infarction Prognosis: Evaluating the Performance of Machine Learning and Deep Learning Models.Current cardiology reviews · 2026Review
- Echo-Vision-FM: a pre-training and fine-tuning framework for echocardiogram video vision foundation model.Nature communications · 2025Article
- A hybrid spatial and temporal attention driven network for left ventricular function assessment using echocardiography.Scientific reports · 2025Article
- Use of artificial intelligence for detecting left ventricular dysfunction and predicting incident heart failure risk.ESC heart failure · 2025Review
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Authors and funding
14 authors.
Funding
Abstract
backgroundDeep learning interpretation of echocardiographic images may facilitate automated assessment of cardiac structure and function.
objectivesWe developed a deep learning model to interpret echocardiograms and examined the association of deep learning-derived echocardiographic measures with incident outcomes.
methodsWe trained and validated a 3-dimensional convolutional neural network model for echocardiographic view classification and quantification of left atrial dimension, left ventricular wall thickness, chamber diameter, and ejection fraction. The training sample comprised 64,028 echocardiograms (n = 27,135) from a retrospective multi-institutional ambulatory cardiology electronic health record sample. Validation was performed in a separate longitudinal primary care sample and an external health care system data set. Cox models evaluated the association of model-derived left heart measures with incident outcomes.
resultsDeep learning discriminated echocardiographic views (area under the receiver operating curve >0.97 for parasternal long axis, apical 4-chamber, and apical 2-chamber views vs human expert annotation) and quantified standard left heart measures (R
conclusionsDeep learning echocardiographic interpretation accurately quantified standard measures of left heart structure and function, which in turn were associated with future clinical outcomes. Deep learning may enable automated echocardiogram interpretation and disease prediction at scale.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.