A Multi-Center Clinical Trial for Wireless Stethoscope-Based Diagnosis and Prognosis of Children Community-Acquired Pneumonia

• Published in: IEEE transactions on biomedical engineering Vol. 70; no. 7; pp. 2215 - 2226
• Main Authors: Huang, Dongmin, Wang, Lingwei, Wang, Wenjin
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Audio data; Child; Children; Clinical trials; Community-acquired pneumonia; Diagnosis; Hospitals; Humans; Lung; lung sound analysis; Lungs; machine learning; Medical instruments; Monitoring methods; Pediatrics; Pneumonia; Pneumonia - diagnosis; Pneumonia - pathology; Prognosis; Prognostics and health management; Respiratory Sounds - diagnosis; Stethoscope; Stethoscopes; Tachypnea; Wireless communication
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2023.3239372

Community-Acquired Pneumonia (CAP) is a significant cause of child mortality globally, due to the lack of ubiquitous monitoring methods. Clinically, the wireless stethoscope can be a promising solution since lung sounds with crackles and tachypnea are considered as the typical symptoms of CAP. In this paper, we carried out a multi-center clinical trial in four hospitals to investigate the feasibility of using a wireless stethoscope for children CAP diagnosis and prognosis. The trial collects both the left and right lung sounds from children with CAP at the time of diagnosis, improvement, and recovery. A bilateral pulmonary audio-auxiliary model (BPAM) is proposed for lung sound analysis. It learns the underlying pathological paradigm for the CAP classification by mining the contextual information of audio while preserving the structured information of breathing cycle. The clinical validation shows that the specificity and sensitivity of BPAM are over 92% in both the CAP diagnosis and prognosis for the subject-dependent experiment, over 50% in CAP diagnosis and 39% in CAP prognosis for the subject-independent experiment. Almost all benchmarked methods have improved performance by fusing left and right lung sounds, indicating the direction of hardware design and algorithmic improvement.