What question did this study set out to answer?

The aim is to explore the application of machine learning for analyzing ECG and PCG signals to detect rheumatic heart disease.

January 10, 2026Computer Methods and Programs in Biomedicine Update0 citationsOpen Access

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

DODamilare Emmanuel Olatunji JZJulius Dona ZannuCarnegie Mellon University Africa CMCarine Pierrette MukamakuzaCentral South University

Key Points

The aim is to explore the application of machine learning for analyzing ECG and PCG signals to detect rheumatic heart disease.
Reviewed literature from 2015 to 2025 on machine learning techniques for ECG and PCG signal analysis.

Structured PICO

Do machine learning models analyzing ECG and PCG signals accurately detect rheumatic heart disease?

Population

37 peer-reviewed studies from 2015 to 2025 analyzing electrocardiogram (ECG) and phonocardiogram (PCG) data for rheumatic heart disease (RHD) detection

Intervention

Machine learning (ML) and deep learning models (e.g., CNNs) analyzing ECG and PCG signals

Outcome

Diagnostic performance metrics including accuracy, F1-score, and AUROCsurrogate

Machine learning analysis of ECG and PCG signals shows high diagnostic accuracy for rheumatic heart disease, but clinical readiness is limited by a lack of external validation and reliance on small, private, single-center datasets.

Abstract

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.

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