Artificial Intelligence-Based Algorithms for Early Detection of Heart Failure Using Electrocardiography and Echocardiography

Heart failure (HF) affects over 6 crore people globally and is associated with substantial morbidity, mortality, and healthcare costs. Early identification of structural and functional cardiac abnormalities is essential for timely intervention and prognosis improvement. Electrocardiography (ECG) and echocardiography (Echo) remain the primary non-invasive tools for HF diagnosis, yet their effectiveness can be limited by inter-observer variability, operator dependence, and challenges in detecting subclinical disease. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL) techniques, have emerged as powerful tools for pattern recognition and automated interpretation of large-scale cardiovascular data, potentially enabling earlier and more accurate detection of HF. We conducted a comprehensive systematic review following PRISMA guidelines. Literature searches were performed across PubMed, Embase, IEEE Xplore, Scopus, and Cochrane Library for studies published until [insert cut-off date], evaluating AI-based algorithms for the early detection or classification of HF using ECG and/or echocardiography. Eligible studies included adult populations, focused on AI techniques (supervised or unsupervised), and reported diagnostic performance metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Two independent reviewers performed screening, data extraction, and quality assessment using the PROBAST tool. Most studies utilized convolutional neural networks (CNNs), support vector machines (SVMs), or recurrent neural networks (RNNs). ECG-based AI models demonstrated strong capabilities in detecting left ventricular systolic dysfunction, with AUCs ranging from 0.86 to 0.96, even in asymptomatic individuals. Echo-based models performed well in classifying HF phenotypes (HFpEF, HFrEF), with AUCs up to 0.98. However, considerable heterogeneity existed in data sources, annotation standards, and validation approaches. Only a minority of studies conducted external or prospective validation. AI-based algorithms show considerable promise in enabling early, automated, and non-invasive detection of heart failure from ECG and echocardiographic data. These models may supplement clinical decision-making, reduce diagnostic delays, and improve risk stratification. However, current evidence is limited by methodological variability and lack of large-scale prospective validation. Future research should focus on real-world implementation, model interpretability, and integration into clinical workflows.