Deep learning-based lung sound analysis for intelligent stethoscope  

作　　者：Dong-Min Huang Jia Huang Kun Qiao Nan-Shan Zhong Hong-Zhou Lu Wen-Jin Wang 

机构地区：[1]Department of Biomedical Engineering,Southern University of Science and Technology,Shenzhen 518055,Guangdong,China [2]The Third People’s Hospital of Shenzhen,Shenzhen 518112,Guangdong,China [3]Guangzhou Institute of Respiratory Health,China State Key Laboratory of Respiratory Disease,National Clinical Research Center for Respiratory Disease,the First Affiliated Hospital of Guangzhou Medical University,Guangzhou 510120,China

出　　处：《Military Medical Research》2024年第4期567-588,共22页军事医学研究（英文版）

基　　金：This work is supported by the National Key Research and Development Program of China(2022YFC2407800);the General Program of National Natural Science Foundation of China(62271241);the Guangdong Basic and Applied Basic Research Foundation(2023A1515012983);the Shenzhen Fundamental Research Program(JCYJ20220530112601003).

摘　　要：Auscultation is crucial for the diagnosis of respiratory system diseases.However,traditional stethoscopes have inherent limitations,such as inter-listener variability and subjectivity,and they cannot record respiratory sounds for offline/retrospective diagnosis or remote prescriptions in telemedicine.The emergence of digital stethoscopes has overcome these limitations by allowing physicians to store and share respiratory sounds for consultation and education.On this basis,machine learning,particularly deep learning,enables the fully-automatic analysis of lung sounds that may pave the way for intelligent stethoscopes.This review thus aims to provide a comprehensive overview of deep learning algorithms used for lung sound analysis to emphasize the significance of artificial intelligence(AI)in this field.We focus on each component of deep learning-based lung sound analysis systems,including the task categories,public datasets,denoising methods,and,most importantly,existing deep learning methods,i.e.,the state-of-the-art approaches to convert lung sounds into two-dimensional(2D)spectrograms and use convolutional neural networks for the end-to-end recognition of respiratory diseases or abnormal lung sounds.Additionally,this review highlights current challenges in this field,including the variety of devices,noise sensitivity,and poor interpretability of deep models.To address the poor reproducibility and variety of deep learning in this field,this review also provides a scalable and flexible open-source framework that aims to standardize the algorithmic workflow and provide a solid basis for replication and future extension:https://github.com/contactless-healthcare/Deep-Learning-for-Lung-Sound-Analysis.

关 键 词：Deep learning Lung sound analysis Respiratory sounds 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TN912.3[自动化与计算机技术—控制科学与工程] R318.6[电子电信—通信与信息系统]