用机器学习分析心电图和心音图，助力资源匮乏地区早筛风湿性心脏病。
Machine Learning-Based Analysis of ECG and PCG Signals for Rheumatic Heart Disease Detection: A Scoping Review (2015-2025)
- 用卷积神经网络分析ECG和PCG信号，实现高精度自动检测
- 模型平均准确率达97.75%，但多数研究未在真实环境验证
- 适合关注临床落地的医疗AI研究者与公共卫生政策制定者
人工智能赋能的电子听诊器为风湿性心脏病（RHD）筛查提供了有前景的替代方案，尤其适用于诊断资源匮乏地区。早期发现至关重要，但超声心动图作为金标准，因成本高和专业人员不足，在低资源环境中难以普及。本综述系统分析了2015至2025年间37篇同行评审研究，聚焦机器学习（ML）在心电图（ECG）与心音图（PCG）数据分析中的应用，支持全面、可扩展的RHD筛查，契合世界心脏联合会‘25 by 25’降低RHD死亡率的目标。采用PRISMA-ScR指南，数据来源包括PubMed、IEEE Xplore、Scopus和Embase。近年来，卷积神经网络（CNNs）占据主导地位，实现中位准确率97.75%、F1-score 0.95、AUROC 0.89。然而挑战依然存在：73%的研究使用单中心数据集，81.1%依赖私有数据，仅10.8%进行外部验证，且无一项评估成本效益。尽管45.9%的研究来自流行区，但多数未考虑人口多样性或实施可行性。这些差距凸显出模型性能与临床可用性之间的脱节。弥合这一鸿沟需建立标准化基准数据集、在流行区开展前瞻性试验，并扩大验证范围。若解决上述问题，基于AI的听诊将可能变革欠发达地区的心血管诊疗，推动早期发现。本综述还提供了构建可及性ML-RHD筛查工具的实用建议，旨在缩小低资源地区诊断差距——传统听诊可能漏诊高达90%的病例，而超声仍难企及。
原文摘要 · Abstract (English)
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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