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

Huang, Dongmin; Wang, Lingwei; Wang, Wenjin

doi:10.1109/tbme.2023.3239372

Cited by 7 publications

(5 citation statements)

References 70 publications

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“…A clinical trial showed that the diagnostic accuracy of medical interns was improved upon using LITTMAN 3200 compared to the traditional acoustic stethoscope [ 62 ]. Some studies also used machine learning to automatically detect abnormal lung sounds and diagnose lung diseases in offline clinical studies, wherein the 3M LITTMAN 3200 was applied to collect and transmit lung sounds [ 10 , 63 , 64 ].…”

Section: Digital Stethoscopesmentioning

confidence: 99%

“…This section outlines the existing deep-learning methods for lung sound analysis [ 10 , 22 – 27 , 33 , 72 , 77 , 82 , 91 , 92 , 117 , 122 – 157 ], as shown in Table 3 . Many aspects of deep learning-based lung analysis are overviewed: basic model selection, the advancement of medical tasks, and limitations and future directions.…”

Section: Deep Learning In Lung Sound Analysismentioning

confidence: 99%

“…This approach demonstrated the validity of pre-trained deep-learning architectures in RDR. Huang et al [ 10 ] proposed a hybrid model based on pre-trained VGGish networks and BiLSTM to identify the severity of community-acquired pneumonia among children, including pneumonia-confirmation, spontaneous resolution, and recovery. Altan et al [ 143 ] adopted the cuboid and octant-based quantization methods to extract characteristic abnormalities from a 3D-second order difference plot, then used a deep extreme learning machine classifier to separate five COPD severities.…”

Section: Deep Learning In Lung Sound Analysismentioning

confidence: 99%

“…To date, there are two data-splitting strategies for lung sound analysis: (1) subject-dependent experiment [ 22 , 130 , 131 ] that randomly splits the entire dataset into training and testing sets. Here, the data from one subject exist in both the training set and the testing set; and (2) subject-independent experiment [ 10 , 24 , 175 ] that splits the entire dataset into training and testing sets in a subject-wise manner. Here, the data from one subject only appear in the training set or testing set to implement the cross-subject benchmark.…”

Section: Open-source Frameworkmentioning

confidence: 99%

“…Although the stethoscope has been widely used in clinics, it has several associated challenges. First, the interpretation of lung sounds requires a trained paramedic, limiting stethoscope use in low-resource areas [ 10 ]. Second, the medical-decisions made based on auscultation are subject to inter-listener variability in proficiency [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Deep learning-based lung sound analysis for intelligent stethoscope

Huang,

Qiao

et al. 2023

Military Med Res

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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.

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