ArticleNPJ cardiovascular health2025
Non-genetic factors determine deep learning identified ECG differences between black and white healthy subjects.
Article in NPJ cardiovascular health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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
6 citing papers in PubMed.
- Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease.Diagnostics (Basel, Switzerland) · 2026Review
- Wearable Electrocardiogram Technologies for the Early Detection of Acute Coronary Syndromes.JACC. Asia · 2026Review
- Non-specific ECG ST-T abnormalities: cardiac repolarisation memory assessment.Heart (British Cardiac Society) · 2026Article
- Artificial intelligence electrocardiography for left ventricular systolic dysfunction demonstrates preserved performance across demographic training imbalances.European heart journal. Digital health · 2026Article
- Personalized artificial intelligence based left ventricular ejection fraction and systolic dysfunction assessment.NPJ digital medicine · 2026Article
- Total product lifecycle regulatory considerations and recommendations for generative AI-enabled medical devices.European heart journal. Digital health · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Artificial intelligence (AI) models capable of detecting a patient's reported race from medical data raise important concerns around fairness and equity. In this study, we investigated whether machine learning models could identify race-based differences in electrocardiograms (ECGs) from healthy Black and White individuals and explored the origins of these differences. We analyzed approximately 10 million ECG traces from 1.76 million subjects across multiple institutions. A convolutional neural network (CNN)-based classifier was developed and optimized for various configurations, including network depth, fusion strategy, and input format. The best-performing model, a 1-layer late fusion CNN using median beats as input, achieved an AUC of 86.17 (±0.34). Performance was consistent across sexes (AUC≈86%), and analysis suggested race-related ECG signatures appear after birth. Socioeconomic status influenced model accuracy, and interpretability analyses revealed the QRS complex as a key contributor. These findings highlight the presence of non-genetic, race-associated patterns in ECGs.
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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.