Deep learning-based lung sound analysis for intelligent stethoscope

Huang, Dong-Min1; Huang, Jia2; Qiao, Kun2; Zhong, Nan-Shan3; Lu, Hong-Zhou2; Wang, Wen-Jin1

2023-09-26

10.1186/s40779-023-00479-3

MILITARY MEDICAL RESEARCH   Impact Factor & Quartile

2095-7467

2054-9369

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.

Deep learning Lung sound analysis Respiratory sounds

SCI

English

First ; Corresponding

General & Internal Medicine

Medicine, General & Internal

WOS:001070387000001

BMC

Web of Science

1.Southern Univ Sci & Technol, Dept Biomed Engn, Shenzhen 518055, Guangdong, Peoples R China
2.Third Peoples Hosp Shenzhen, Shenzhen 518112, Guangdong, Peoples R China
3.Guangzhou Med Univ, Affiliated Hosp 1, Natl Clin Res Ctr Resp Dis, China State Key Lab Resp Dis,Guangzhou Inst Resp H, Guangzhou 510120, Peoples R China

Huang, Dong-Min,Huang, Jia,Qiao, Kun,et al. Deep learning-based lung sound analysis for intelligent stethoscope[J]. MILITARY MEDICAL RESEARCH,2023,10(1).

Huang, Dong-Min,Huang, Jia,Qiao, Kun,Zhong, Nan-Shan,Lu, Hong-Zhou,&Wang, Wen-Jin.(2023).Deep learning-based lung sound analysis for intelligent stethoscope.MILITARY MEDICAL RESEARCH,10(1).

Huang, Dong-Min,et al."Deep learning-based lung sound analysis for intelligent stethoscope".MILITARY MEDICAL RESEARCH 10.1(2023).