Precision Prediction of Incident Heart Failure: The Clinical Integration of AI-Enabled Electrocardiography

The ECG2HF model, a novel convolutional neural network, demonstrates superior discrimination for 10-year incident heart failure (HF) with AUCs ranging from 0.84 to 0.86 across diverse health systems.
Unlike previous proprietary algorithms, ECG2HF is publicly available, facilitating generalizability and clinical transparency in cardiovascular risk assessment.
AI-enabled ECG analysis significantly improves net reclassification when compared to the 15-component Pooled Cohorts Equations to Prevent HF (PCE-HF), identifying high-risk individuals missed by traditional clinical models.
The integration of AI into standard diagnostics, such as ECG and mammography, represents a paradigm shift toward opportunistic screening for systemic cardiovascular morbidity.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

The ECG2HF model, a novel convolutional neural network, demonstrates superior discrimination for 10-year incident heart failure (HF) with AUCs ranging from 0.84 to 0.86 across diverse health systems.
Unlike previous proprietary algorithms, ECG2HF is publicly available, facilitating generalizability and clinical transparency in cardiovascular risk assessment.
AI-enabled ECG analysis significantly improves net reclassification when compared to the 15-component Pooled Cohorts Equations to Prevent HF (PCE-HF), identifying high-risk individuals missed by traditional clinical models.
The integration of AI into standard diagnostics, such as ECG and mammography, represents a paradigm shift toward opportunistic screening for systemic cardiovascular morbidity.

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