Abstract:
Community-Acquired Pneumonia (CAP) is a significant cause of child mortality globally, due to the lack of ubiquitous monitoring methods. Clinically, the wireless stethosc...Show MoreMetadata
Abstract:
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.
Published in: IEEE Transactions on Biomedical Engineering ( Volume: 70, Issue: 7, July 2023)
Funding Agency:
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- IEEE Keywords
- Index Terms
- Multicenter Trial ,
- Community-acquired Pneumonia ,
- Multicenter Clinical Trial ,
- Structural Information ,
- Contextual Information ,
- Left Lung ,
- Crackles ,
- Breath Sounds ,
- Stethoscope ,
- Diagnosis Of Community-acquired Pneumonia ,
- Machine Learning ,
- Immunodeficiency ,
- Deep Learning ,
- Convolutional Neural Network ,
- Support Vector Machine ,
- Chest X-ray ,
- K-nearest Neighbor ,
- Transfer Learning ,
- Autoencoder ,
- Stage Of Treatment ,
- Convolutional Neural Network Layers ,
- Short-time Fourier Transform ,
- Different Stages Of Treatment ,
- Detection Of Abnormalities ,
- Abnormal Sounds ,
- Potential Space ,
- Balance Performance ,
- Community-acquired Pneumonia Patients ,
- Chest X-ray Images ,
- Respiratory Cycle
- Author Keywords
- MeSH Terms
- Humans ,
- Child ,
- Stethoscopes ,
- Respiratory Sounds ,
- Pneumonia ,
- Lung
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Multicenter Trial ,
- Community-acquired Pneumonia ,
- Multicenter Clinical Trial ,
- Structural Information ,
- Contextual Information ,
- Left Lung ,
- Crackles ,
- Breath Sounds ,
- Stethoscope ,
- Diagnosis Of Community-acquired Pneumonia ,
- Machine Learning ,
- Immunodeficiency ,
- Deep Learning ,
- Convolutional Neural Network ,
- Support Vector Machine ,
- Chest X-ray ,
- K-nearest Neighbor ,
- Transfer Learning ,
- Autoencoder ,
- Stage Of Treatment ,
- Convolutional Neural Network Layers ,
- Short-time Fourier Transform ,
- Different Stages Of Treatment ,
- Detection Of Abnormalities ,
- Abnormal Sounds ,
- Potential Space ,
- Balance Performance ,
- Community-acquired Pneumonia Patients ,
- Chest X-ray Images ,
- Respiratory Cycle
- Author Keywords
- MeSH Terms
- Humans ,
- Child ,
- Stethoscopes ,
- Respiratory Sounds ,
- Pneumonia ,
- Lung