ArticleNature cardiovascular research2025
Identification of hypertrophic cardiomyopathy on electrocardiographic images with deep learning.
Article in Nature cardiovascular research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
25 citing papers in PubMed.
- Point-of-care echocardiography screening for hypertrophic cardiomyopathy using automated deep-learning analysis.European heart journal. Digital health · 2026Article
- Artificial intelligence and the evolution of the electrocardiogram: from cardiovascular diagnostic tool to digital biomarker.European heart journal. Digital health · 2026Review
- Disease-focused evaluation of AI-ECG tools: clarifying study design, findings, and the principles of open science.European heart journal. Digital health · 2026Article
- Open science requires trust and rigour: a framework for responsible evaluation of shared AI-ECG tools.European heart journal. Digital health · 2026Article
- Artificial Intelligence-enhanced Electrocardiography for Heart Failure Screening and Risk Stratification.Current heart failure reports · 2026Review
- Artificial Intelligence-Enabled Electrocardiography in Practice: A State-of-the-Art Review.Korean circulation journal · 2026Review
- Evaluation of artificial intelligence-based electrocardiogram analysis tools in patients with hypertrophic cardiomyopathy.European heart journal. Digital health · 2026Article
- AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions.International journal of general medicine · 2026Review
- Reliability of Artificial Intelligence-enhanced Electrocardiography.medRxiv : the preprint server for health sciences · 2025Article
- Phenotypic Selectivity of Artificial Intelligence-Enhanced Electrocardiography in Cardiovascular Diagnosis and Risk Prediction.Circulation · 2025Article
- Artificial intelligence-enhanced electrocardiography and sudden death risk factors in cardiac arrest survivors diagnosed with hypertrophic cardiomyopathy.Heart rhythm O2 · 2025Article
- Classification, Diagnosis, and Prognosis of Cardiomyopathy: A Comprehensive Narrative Review.Reviews in cardiovascular medicine · 2025Review
- Artificial Intelligence-Enabled Prediction of Heart Failure Risk From Single-Lead Electrocardiograms.JAMA cardiology · 2025Article
- Automated Deep Learning Phenotyping of Tricuspid Regurgitation in Echocardiography.JAMA cardiology · 2025Article
- The emerging role of AI in transforming cardiovascular care.Future cardiology · 2025Article
- Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images: PRESENT SHD.Journal of the American College of Cardiology · 2025Article
- Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study.European heart journal · 2025Article
- A Multicenter Evaluation of the Impact of Therapies on Deep Learning-Based Electrocardiographic Hypertrophic Cardiomyopathy Markers.The American journal of cardiology · 2025Article
- An Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images: PRESENT SHD.medRxiv : the preprint server for health sciences · 2024Article
- Artificial Intelligence Enabled Prediction of Heart Failure Risk from Single-lead Electrocardiograms.medRxiv : the preprint server for health sciences · 2024Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Hypertrophic cardiomyopathy (HCM) is frequently underdiagnosed. Although deep learning (DL) models using raw electrocardiographic (ECG) voltage data can enhance detection, their use at the point of care is limited. Here we report the development and validation of a DL model that detects HCM from images of 12-lead ECGs across layouts. The model was developed using 124,553 ECGs from 66,987 individuals at the Yale New Haven Hospital (YNHH), with HCM features determined by concurrent imaging (cardiac magnetic resonance (CMR) or echocardiography). External validation included ECG images from MIMIC-IV, the Amsterdam University Medical Center (AUMC) and the UK Biobank (UKB), where HCM was defined by CMR (YNHH, MIMIC-IV and AUMC) and diagnosis codes (UKB). The model demonstrated robust performance across image formats and sites (areas under the receiver operating characteristic curve (AUROCs): 0.95 internal testing; 0.94 MIMIC-IV; 0.92 AUMC; 0.91 UKB). Discriminative features localized to anterior/lateral leads (V4 and V5) regardless of layout. This approach enables scalable, image-based screening for HCM across clinical settings.
Indexed as
Identifiers
40696040What 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.