ECG classification with convolutional neural networks demonstrates resilience to sex-imbalances in data

2025-09-02 cardiovascular medicine Title + abstract only

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BackgroundMany ECG-AI models have been developed to predict a wide range of cardiovascular outcomes. The underrepresentation of women in cardiovascular disease studies has raised concerns if these models are equally predictive in women as compared to men. We tested the effect of sex-imbalance in training datasets on predictive performance of ECG-AI models, investigating imbalance in representation (ratio women-to-men), as well as in outcome prevalence, and percentage of misclassification. Metho...

ECG classification with convolutional neural networks demonstrates resilience to sex-imbalances in data

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