Tuberculosis and Respiratory Diseases

The Korean Academy of Tuberculosis and Respiratory Diseases (대한결핵및호흡기학회)
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Evolution of the Stethoscope: Advances with the Adoption of Machine Learning and Development of Wearable Devices

  • Yoonjoo Kim (Division of Pulmonology, Department of Internal Medicine, Chungnam National University College of Medicine) ;
  • YunKyong Hyon (Division of Industrial Mathematics, National Institute for Mathematical Sciences) ;
  • Seong-Dae Woo (Division of Pulmonology, Department of Internal Medicine, Chungnam National University College of Medicine) ;
  • Sunju Lee (Division of Industrial Mathematics, National Institute for Mathematical Sciences) ;
  • Song-I Lee (Division of Pulmonology, Department of Internal Medicine, Chungnam National University College of Medicine) ;
  • Taeyoung Ha (Division of Industrial Mathematics, National Institute for Mathematical Sciences) ;
  • Chaeuk Chung (Division of Pulmonology, Department of Internal Medicine, Chungnam National University College of Medicine)
  • Received : 2023.05.09
  • Accepted : 2023.08.15
  • Published : 2023.10.31
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Abstract

The stethoscope has long been used for the examination of patients, but the importance of auscultation has declined due to its several limitations and the development of other diagnostic tools. However, auscultation is still recognized as a primary diagnostic device because it is non-invasive and provides valuable information in real-time. To supplement the limitations of existing stethoscopes, digital stethoscopes with machine learning (ML) algorithms have been developed. Thus, now we can record and share respiratory sounds and artificial intelligence (AI)-assisted auscultation using ML algorithms distinguishes the type of sounds. Recently, the demands for remote care and non-face-to-face treatment diseases requiring isolation such as coronavirus disease 2019 (COVID-19) infection increased. To address these problems, wireless and wearable stethoscopes are being developed with the advances in battery technology and integrated sensors. This review provides the history of the stethoscope and classification of respiratory sounds, describes ML algorithms, and introduces new auscultation methods based on AI-assisted analysis and wireless or wearable stethoscopes.

Keywords

Acknowledgement

This study was supported by a 2021-Grant from the Korean Academy of Tuberculosis and Respiratory Diseases, National Research Foundation of Korea (2022R1F1A1076515), and National Institute for Mathematical Sciences (NIMS) grant funded by the Korean government (No. B23910000).

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