ArticleNPJ digital medicine2023
A deep learning-based electrocardiogram risk score for long term cardiovascular death and disease.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it, 52 citations in OpenAlex.
- Artificial Intelligence Applied to Electrocardiogram-Based Cardiovascular Risk Assessment: A Systematic Review.Arquivos brasileiros de cardiologia · 2026Pooled it
- Wearable Electronics for Precision Diagnosis Through Advanced Manufacturing and Integration.Nano-micro letters · 2026Review
- ECG Signatures and Long-Term Ischemic Stroke Risk: A Deep Learning Analysis of 200,000 Patients.Journal of the American College of Cardiology · 2026Article
- Article
- Wearable Flexible Sensors for Cardiovascular Disease Monitoring.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Modeling day-long ECG signals to predict heart failure risk with explainable AI.NPJ digital medicine · 2026Article
- An ECG foundation model for generalizable cardiac function prediction across the lifespan.medRxiv : the preprint server for health sciences · 2026Article
- CARDIAC-FM: A Multimodal Foundation Model for Cardiovascular Risk Prediction Using ECG and Cardiac MRI.medRxiv : the preprint server for health sciences · 2026Article
- Artificial Intelligence-Enabled Electrocardiography in Practice: A State-of-the-Art Review.Korean circulation journal · 2026Review
- The Prognostic Ability of ECG Findings in Predicting Cardiovascular Events: A Five-Year Nested Case-Cohort Study in an Iranian Population (Shiraz Heart Study).Health science reports · 2026Article
- From Exposure to Atherosclerosis: Mechanistic Insights into Phthalate-Driven Ischemic Heart Disease and Prevention Strategies.Life (Basel, Switzerland) · 2026Review
- Development and explanation of electrocardiogram-based deep learning for predicting short-term mortality in heart failure patients.Journal of global health · 2026Article
- Digital ECG detection of flat T waves predicts cardiovascular events in high-risk patients: the Coupling Study.Hypertension research : official journal of the Japanese Society of Hypertension · 2026Article
- Long-term, ambulatory 12-lead ECG from a single non-standard lead using perceptual reconstruction.medRxiv : the preprint server for health sciences · 2025Article
- Deep survival analysis from adult and pediatric electrocardiograms: a multi-center benchmark study.BioData mining · 2025Article
- Baseline ECG and Cardiovascular Outcomes in People With HIV: Insights From REPRIEVE.Journal of the American Heart Association · 2025Observational
- Evaluating artificial intelligence-enabled medical tests in cardiology: Best practice.International journal of cardiology. Heart & vasculature · 2025Review
- Transforming Population Health Screening for Atherosclerotic Cardiovascular Disease with AI-Enhanced ECG Analytics: Opportunities and Challenges.Current atherosclerosis reports · 2025Review
- ViViEchoformer: Deep Video Regressor Predicting Ejection Fraction.Journal of imaging informatics in medicine · 2025Article
- A deep learning phenome wide association study of the electrocardiogram.European heart journal. Digital health · 2025Article
Corrections and comments
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Authors and funding
17 authors at 4 institutions in 1 country.
Funding
Abstract
The electrocardiogram (ECG) is the most frequently performed cardiovascular diagnostic test, but it is unclear how much information resting ECGs contain about long term cardiovascular risk. Here we report that a deep convolutional neural network can accurately predict the long-term risk of cardiovascular mortality and disease based on a resting ECG alone. Using a large dataset of resting 12-lead ECGs collected at Stanford University Medical Center, we developed SEER, the Stanford Estimator of Electrocardiogram Risk. SEER predicts 5-year cardiovascular mortality with an area under the receiver operator characteristic curve (AUC) of 0.83 in a held-out test set at Stanford, and with AUCs of 0.78 and 0.83 respectively when independently evaluated at Cedars-Sinai Medical Center and Columbia University Irving Medical Center. SEER predicts 5-year atherosclerotic disease (ASCVD) with an AUC of 0.67, similar to the Pooled Cohort Equations for ASCVD Risk, while being only modestly correlated. When used in conjunction with the Pooled Cohort Equations, SEER accurately reclassified 16% of patients from low to moderate risk, uncovering a group with an actual average 9.9% 10-year ASCVD risk who would not have otherwise been indicated for statin therapy. SEER can also predict several other cardiovascular conditions such as heart failure and atrial fibrillation. Using only lead I of the ECG it predicts 5-year cardiovascular mortality with an AUC of 0.80. SEER, used alongside the Pooled Cohort Equations and other risk tools, can substantially improve cardiovascular risk stratification and aid in medical decision making.
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Registered trials
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.