Hsieh Yi-Tang, Liu Hsien-Kuan, Wu Jiunn-Ren, Yan Ting-Yu, Huang Yu-Jung, Guo Mei Hui, Yang Ming-Chun
Department of Applied Mathematics, National Sun Yat-sen University, Kaohsiung, Taiwan.
Department of Pediatrics, E-DA Hospital, I-Shou University, No. 1, Yida Road, Yanchao District, Kaohsiung, Taiwan.
Sci Rep. 2025 May 31;15(1):19155. doi: 10.1038/s41598-025-04746-2.
Congenital heart disease affects approximately 1% of children worldwide, with a number of cases in resource-limited settings remaining undiagnosed through school age. While cardiac auscultation is a key screening method, its effectiveness varies widely, depending on practitioner expertise. This study introduces an innovative artificial intelligence (AI) approach combining conventional machine learning and deep learning techniques to improve heart murmur detection in pediatric populations. By developing an integrated Residual-Recurrent Neural Networks model and analyzing heart sound recordings from 500 pediatric participants, we achieved remarkable diagnostic performance in real-world pediatric clinical settings. At the single recording-level, the model achieved an accuracy of 88.5%, sensitivity of 85.5%, and specificity of 90.7%. Performance improved at the participant-level, with an accuracy of 90.0%, sensitivity of 88.8%, and specificity of 91.2%. The model showed particularly strong results when tested against the PhysioNet database (accuracy 95.2%, sensitivity 91.6%, and specificity 99.1%). This research provides a compelling proof-of-concept for AI-assisted cardiac screening, potentially revolutionizing early detection strategies in pediatric cardiac diseases.
先天性心脏病影响着全球约1%的儿童，在资源有限的环境中，有许多病例在学龄期仍未得到诊断。虽然心脏听诊是一种关键的筛查方法，但其有效性差异很大，这取决于从业者的专业水平。本研究引入了一种创新的人工智能（AI）方法，该方法结合了传统机器学习和深度学习技术，以改善儿科人群中的心杂音检测。通过开发一个集成的残差循环神经网络模型，并分析来自500名儿科参与者的心音记录，我们在实际的儿科临床环境中取得了显著的诊断性能。在单个记录水平上，该模型的准确率为88.5%，灵敏度为85.5%，特异性为90.7%。在参与者水平上性能有所提高，准确率为90.0%，灵敏度为88.8%，特异性为91.2%。当在PhysioNet数据库上进行测试时，该模型显示出特别强劲的结果（准确率95.2%，灵敏度91.6%，特异性99.1%）。这项研究为人工智能辅助心脏筛查提供了一个令人信服的概念验证，可能会彻底改变儿科心脏病的早期检测策略。
