October 10, 2025Open Access
Non-genetic factors determine deep learning identified ECG differences between black and white healthy subjects
PICO estructurado
Does a deep learning model accurately identify race (Black vs. White) from the ECGs of healthy subjects?
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Población
198,222 healthy subjects (devoid of cardiac or neurological conditions) with 379,909 ECGs, geographically distributed throughout Massachusetts, USA. Excluded multi-racial, 'Other', or unknown race categories.
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Intervención
Convolutional neural network (CNN)-based classifier (optimal model: 1-layer late fusion architecture using median beats as input) to identify race from ECG signals.
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Resultado
Area under the receiver operating characteristic curve (AUC) for race classification (Black vs. White).surrogate
Deep learning models can accurately identify race from the ECGs of healthy subjects, driven by non-genetic factors such as socioeconomic status and age, highlighting potential biases in AI algorithms.
Resumen
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.