用听诊信号识别先天性心脏病，适合资源匮乏地区筛查。
Congenital Heart Disease Classification Using Phonocardiograms: A Scalable Screening Tool for Diverse Environments
- 基于听诊图设计深度学习模型，支持单点或多点采集。
- 在孟加拉主数据集上达94.1%准确率，低质录音仍保持80%准确率。
- 模型轻量易部署，适合基层医疗和全球推广。
先天性心脏病（CHD）需早期发现，尤其在婴幼儿期。本研究提出一种深度学习模型，利用听诊图（PCG）信号检测CHD，聚焦于全球健康应用。模型在孟加拉主数据集上表现优异，准确率达94.1%，灵敏度92.7%，特异性96.3%。在公开的PhysioNet Challenge 2022与2016数据集上也展现出良好泛化能力，适用于不同人群与数据源。评估显示，即使仅使用一个胸腔听诊点，模型准确率仍超85%；在心内科医生判定为非诊断性的低质量录音上，仍能达到80%准确率。研究表明，基于AI的数字听诊器可作为资源受限环境下低成本的CHD初筛工具，提升临床决策支持并改善患者预后。
原文摘要 · Abstract (English)
Congenital heart disease (CHD) is a critical condition that demands early detection, particularly in infancy and childhood. This study presents a deep learning model designed to detect CHD using phonocardiogram (PCG) signals, with a focus on its application in global health. We evaluated our model on several datasets, including the primary dataset from Bangladesh, achieving a high accuracy of 94.1%, sensitivity of 92.7%, specificity of 96.3%. The model also demonstrated robust performance on the public PhysioNet Challenge 2022 and 2016 datasets, underscoring its generalizability to diverse populations and data sources. We assessed the performance of the algorithm for single and multiple auscultation sites on the chest, demonstrating that the model maintains over 85% accuracy even when using a single location. Furthermore, our algorithm was able to achieve an accuracy of 80% on low-quality recordings, which cardiologists deemed non-diagnostic. This research suggests that an AI- driven digital stethoscope could serve as a cost-effective screening tool for CHD in resource-limited settings, enhancing clinical decision support and ultimately improving patient outcomes.
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