Artificial Intelligence–Based Prediction of Individual Response to Cardiovascular Drug Therap, Deep Learning Analysis of ECG and Imaging Data for Early Detection and Prevention of Heart Failure

Abstract

Heart failure (HF) is increasingly managed as a lifelong syndrome where prognosis depends on how early latent myocardial dysfunction is detected and how precisely therapy is matched to an individual phenotype. Recent evidence shows that deep learning can extract clinically actionable signals from electrocardiography (ECG) and cardiac imaging that are not captured by standard interpretation, enabling earlier detection of left ventricular systolic dysfunction and more timely prevention strategies. In parallel, pharmacogenomics has matured into guideline-supported decision rules for several cardiovascular drugs, proving that “individual response” can be predicted and acted upon when the right biological and clinical features are integrated. This article develops a translational framework that unifies multimodal representation learning (ECG plus echocardiography plus structured clinical data) with causal inference to estimate individualized treatment effects and to recommend patient-specific drug strategies. The novelty is a practical architecture for “response-aware cardiology” that couples: (i) early HF risk detection from ECG and imaging, (ii) mechanistic and genomic predictors of drug response, and (iii) calibrated, auditable decision support aligned with contemporary HF guidelines