ArticleNPJ digital medicine2024
Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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The trial behind it
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Who cites it
15 citing papers in PubMed.
- Prediction of Atrial Fibrillation Risk Through the Integration of Genetic Information and Artificial Intelligence-Based Electrocardiogram Data.Journal of arrhythmia · 2026Article
- Data distribution impacts the performance and generalisability of contrastive learning-based foundation models of electrocardiograms.PLOS digital health · 2026Article
- Artificial intelligence-enhanced electrocardiography for the prediction of future type 2 diabetes mellitus: a model-development and multicentre validation study.European heart journal. Digital health · 2026Article
- Advances in the Interpretation of the Electrocardiogram by Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2026Review
- Age-dependent obesity paradox in acute myocardial infarction prognosis: a cohort study of body mass index and recurrent myocardial infarction.International journal of obesity (2005) · 2026Article
- Temporal trends in myocardial ischemia risk estimated from 12-lead electrocardiograms using deep learning in individuals with suspected cancer during health checkups.Cardio-oncology (London, England) · 2026Article
- Sympathetic-like-integrated engineered heart tissue models AGEs-induced adverse remodeling.Cardiovascular diabetology · 2026Article
- Tele-Electrocardiography and Mortality: Clinical Outcomes in Digital Electrocardiography Cohort-Data from Belo Horizonte, Brazil (CODE-BH).Global heart · 2026Article
- The cost of explainability in artificial intelligence-enhanced electrocardiogram models.NPJ digital medicine · 2025Article
- A foundation transformer model with self-supervised learning for ECG-based assessment of cardiac and coronary function.NEJM AI · 2025Article
- Artificial Intelligence-Enhanced Electrocardiography for Complete Heart Block Risk Stratification.JAMA cardiology · 2025Article
- Unsupervised feature extraction using deep learning empowers discovery of genetic determinants of the electrocardiogram.Genome medicine · 2025Article
- Transforming Population Health Screening for Atherosclerotic Cardiovascular Disease with AI-Enhanced ECG Analytics: Opportunities and Challenges.Current atherosclerosis reports · 2025Review
- Upscaling a regional telecardiology service to a nationwide coverage and beyond: the experience of the Telehealth Network of Minas Gerais.BMJ global health · 2025Article
- Artificial intelligence bias in the prediction and detection of cardiovascular disease.NPJ cardiovascular health · 2024Review
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
The electrocardiogram (ECG) can capture obesity-related cardiac changes. Artificial intelligence-enhanced ECG (AI-ECG) can identify subclinical disease. We trained an AI-ECG model to predict body mass index (BMI) from the ECG alone. Developed from 512,950 12-lead ECGs from the Beth Israel Deaconess Medical Center (BIDMC), a secondary care cohort, and validated on UK Biobank (UKB) (n = 42,386), the model achieved a Pearson correlation coefficient (r) of 0.65 and 0.62, and an R
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What Socratic holds
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 Socratic graph.