Deep Learning for Respiratory Sound Analysis: A Systematic Review and Meta-Analysis (2019–2024)

Shaima’a Mohammed Nasser Al-Jabali; Farhan Nashwan; Waleed M. Altalabi

doi:10.59167/g1qw6p23

Authors

Shaima’a Mohammed Nasser Al-Jabali Information Technology Department, Faculty of Engineering and Information Technology, AL-Qalam University, Ibb, Yemen. Author
Farhan Nashwan Electrical Engineering Department, Faculty of Engineering, IBB University, IBB, Yemen. Author
Waleed M. Altalabi Biomedical Engineering Department, Sana'a Community College, Sana'a, Yemen. Author

DOI:

https://doi.org/10.59167/g1qw6p23

Keywords:

Respiratory sounds, Lung disease classification, Deep learning, Systematic review, Meta-analysis, Multitask learning, Explainable AI

Abstract

Background: Respiratory diseases remain a significant global health burden, particularly in resource-limited settings. Objective: To review and quantitatively analyze deep learning-based models for interpreting respiratory sounds. Methodology: A systematic search was conducted in the PubMed, IEEE Xplore, ScienceDirect, and Scopus databases for the period 2019–2024, using PRISMA 2020 criteria. Of the 678 records accessed, 98 studies met the qualitative criteria, while 42 met the quantitative analysis criteria. Results: An exploratory meta-analysis conducted on a subset of studies reporting native accuracy (k = 9) yielded a pooled accuracy of approximately 90.0% (95% CI: 76.0%–97.0%), with substantial heterogeneity (I² = 99.2%). Conclusion: Deep learning techniques demonstrate promising diagnostic capabilities, but they still face challenges related to reliance on limited databases, poor representation of rare diseases, and difficulty in interpreting their outputs. Implications: Future research should focus on diversifying datasets, enhancing the integration of multimodal data, and developing interpretable or federated learning-based models to support their adoption in clinical settings

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