Deep Learning in Heart Sound Analysis:From Techniques to Clinical Applications  

作　　者：Qinghao Zhao Shijia Geng Boya Wang Yutong Sun Wenchang Nie Baochen Bai Chao Yu Feng Zhang Gongzheng Tang Deyun Zhang Yuxi Zhou Jian Liu Shenda Hong 

机构地区：[1]Department of Cardiology,Peking University People’s Hospital,Beijing,China [2]HeartVoice Medical Technology,Hefei,China [3]Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing),Department of Gastrointestinal Oncology,Peking University Cancer Hospital and Institute,Beijing,China [4]National Institute of Health Data Science,Peking University,Beijing,China [5]Institute of Medical Technology,Health Science Center of Peking University,Beijing,China [6]Department of Computer Science,Tianjin University of Technology,Tianjin,China [7]DCST,BNRist,RIIT,Institute of Internet Industry,Tsinghua University,Beijing,China

出　　处：《Health Data Science》2024年第1期88-109,共22页健康数据科学(英文)

基　　金：supported by the National Natural Science Foundation of China(No.62102008);the Peking University People’s Hospital Scientific Research Development Funds(RDJP2022-39);the Clinical Medicine Plus X-Young Scholars Project of Peking University,and the Fundamental Research Funds for the Central Universities(PKU2024LCXQ030).

摘　　要：Importance:Heart sound auscultation is a routinely used physical examination in clinical practice to identify potential cardiac abnormalities. However, accurate interpretation of heart sounds requires specialized training and experience, which limits its generalizability. Deep learning, a subset of machine learning, involves training artiffcial neural networks to learn from large datasets and perform complex tasks with intricate patterns. Over the past decade, deep learning has been successfully applied to heart sound analysis, achieving remarkable results and accumulating substantial heart sound data for model training. Although several reviews have summarized deep learning algorithms for heart sound analysis, there is a lack of comprehensive summaries regarding the available heart sound data and the clinical applications. Highlights:This review will compile the commonly used heart sound datasets, introduce the fundamentals and state-of-the-art techniques in heart sound analysis and deep learning, and summarize the current applications of deep learning for heart sound analysis, along with their limitations and areas for future improvement. Conclusions:The integration of deep learning into heart sound analysis represents a signiffcant advancement in clinical practice. The growing availability of heart sound datasets and the continuous development of deep learning techniques contribute to the improvement and broader clinical adoption of these models. However, ongoing research is needed to address existing challenges and reffne these technologies for broader clinical use.

关 键 词：DEEP specialized ROUTINE 

分 类 号：H31[语言文字—英语]