Machine learning-based analysis of ECG and PCG signals for rheumatic heart disease detection: A scoping review (2015–2025)

• Review of 37 ML studies highlights gaps in technical success and clinical deployment. • Standardized datasets and trials in endemic regions are urgently needed. • CNNs achieved a median accuracy of 97.75% for RHD detection using ECG/PCG signals. • Only 10.8% of studies included external validation, despite reporting high performance. • 73% relied on single-center data, limiting generalizability to diverse populations. AI-powered stethoscopes offer a promising alternative for screening rheumatic heart disease (RHD), particularly in regions with limited diagnostic infrastructure. Early detection is vital, yet echocardiography, the gold standard tool, remains largely inaccessible in low-resource settings due to cost and workforce constraints. This review systematically examines machine learning (ML) applications from 2015 to 2025 that analyze electrocardiogram (ECG) and phonocardiogram (PCG) data to support accessible, scalable screening of all RHD variants in relation to the World Heart Federation's "25 by 25" goal to reduce RHD mortality. Using PRISMA-ScR guidelines, 37 peer-reviewed studies were selected from PubMed, IEEE Xplore, Scopus, and Embase. Convolutional neural networks (CNNs) dominate recent efforts, achieving a median accuracy of 97.75%, F1-score of 0.95, and AUROC of 0.89. However, challenges remain: 73% of studies used single-center datasets, 81.1% relied on private data, only 10.8% were externally validated, and none assessed cost-effectiveness. Although 45.9% originated from endemic regions, few addressed demographic diversity or implementation feasibility. These gaps underscore the disconnect between model performance and clinical readiness. Bridging this divide requires standardized benchmark datasets, prospective trials in endemic areas, and broader validation. If these issues are addressed, AI-augmented auscultation could transform cardiovascular diagnostics in underserved populations, thereby aiding early detection. This review also offers practical recommendations for building accessible ML-based RHD screening tools, aiming to close the diagnostic gap in low-resource settings where conventional auscultation may miss up to 90% of cases and echocardiography remains out of reach.