Using Deep Learning to Detect Early Heart-Failure Phenotypes from ECG or Imaging

Abstract

Heart failure (HF) remains one of the leading causes of morbidity and mortality worldwide, yet early detection of subclinical phenotypes continues to challenge clinicians. Conventional diagnostic approaches such as echocardiography, electrocardiography (ECG), and biomarker analysis are often limited by inter-observer variability, late-stage detection, and resource constraints. With the emergence of artificial intelligence (AI), particularly deep learning (DL) algorithms, new possibilities have arisen for identifying early and subtle features of cardiac dysfunction from routinely available data.

This article explores how deep learning models—especially convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based architectures—can be trained on ECG signals, cardiac MRI, and echocardiographic imaging to detect early heart-failure phenotypes. It reviews current datasets, model architectures, and validation methods, and discusses clinical applications such as predicting heart-failure with preserved ejection fraction (HFpEF) or reduced ejection fraction (HFrEF) before symptomatic onset. The integration of AI-derived cardiac biomarkers into clinical workflows, challenges in interpretability, and regulatory considerations are analyzed. Finally, the article examines future directions, including federated learning, multimodal fusion of imaging and ECG, and ethical implications in automated cardiac diagnosis.