Automated detection of abnormal respiratory sound from electronic stethoscope and mobile phone using MobileNetV2

• Autorzy: Liao Ximing , Wu Yin , Jiang Nana , Sun Jiaxing , Xu Wujian , Gao Shaoyong , Wang Jun , Li Ting , Wang Kun , Li Qiang

Identyfikatory

DOI

10.1016/j.bbe.2023.11.001

• Języki publikacji: EN

Abstrakty

EN

Auscultation, a traditional clinical examination method using a stethoscope to quickly assess airway abnormalities, remains valuable due to its real-time, non-invasive, and easy-to-perform nature. Recent advancements in computerized respiratory sound analysis (CRSA) have provided a quantifiable approach for recording, editing, and comparing respiratory sounds, also enabling the training of artificial intelligence models to fully excavate the potential of auscultation. However, existing sound analysis models often require complex computations, leading to prolonged processing times and high calculation and memory requirements. Moreover, the limited diversity and scope of available databases limits reproducibility and robustness, mainly relying on small sample datasets primarily collected from Caucasians. In order to overcome these limitations, we developed a new Chinese adult respiratory sound database, LD-DF RSdb, using an electronic stethoscope and mobile phone. By enrolling 145 participants, 9,584 high quality recordings were collected, containing 6,435 normal sounds, 2,782 crackles, 208 wheezes, and 159 combined sounds. Subsequently, we utilized a lightweight neural network architecture, MobileNetV2, for automated categorization of the four types of respiratory sounds, achieving an appreciable overall performance with an AUC of 0.8923. This study demonstrates the feasibility and potential of using mobile phones, electronic stethoscopes, and MobileNetV2 in CRSA. The proposed method offers a convenient and promising approach to enhance overall respiratory disease management and may help address healthcare resource disparities.

Słowa kluczowe

EN

auscultation; electronic stethoscope; respiratory sound; computerized respiratory sound analysis; artificial intelligence; deep learning

PL

osłuchiwanie; stetoskop elektroniczny; szmer oddechowy; sztuczna inteligencja; uczenie głębokie

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2023
• Tom: Vol. 43, no. 4
• Strony: 763--775
• Opis fizyczny: Bibliogr. 42 poz., rys., tab., wykr.

Twórcy

autor

Liao Ximing

• Department of Respiratory and Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China

autor

Wu Yin

• Department of Respiratory and Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China

autor

Jiang Nana

• Department of Respiratory and Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China

autor

Sun Jiaxing

• Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China

autor

Xu Wujian

• Department of Respiratory and Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China

autor

Gao Shaoyong

• Department of Respiratory and Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China

autor

Wang Jun

• Shanghai Hiwein Intelligent Technology Co, Ltd., Shanghai, China
• Ningbo Leader Medical Technology Co, Ltd, Ningbo, Zhejiang, China

autor

Li Ting

• Shanghai Hiwein Intelligent Technology Co, Ltd., Shanghai, China
• Ningbo Leader Medical Technology Co, Ltd, Ningbo, Zhejiang, China

autor

Wang Kun

• Department of Respiratory and Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, China

autor

Li Qiang

• Department of Respiratory and Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, China

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