This paper is in the following e-collection/theme issue:

Digital Health Reviews (1724) Artificial Intelligence (2129) Pulmonology, COPD (195) Respiratory Medicine (157) New Diagnostic Tools and Instruments for Pediatrics (48) Computer-Aided Diagnosis (251) Applications of AI (287)

Published on in Vol 27 (2025)

This is a member publication of

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66491, first published .
Artificial Intelligence Models for Pediatric Lung Sound Analysis: Systematic Review and Meta-Analysis

Artificial Intelligence Models for Pediatric Lung Sound Analysis: Systematic Review and Meta-Analysis

Artificial Intelligence Models for Pediatric Lung Sound Analysis: Systematic Review and Meta-Analysis

Authors of this article:

Ji Soo Park1 Author Orcid Image ;   Sa-Yoon Park2, 3 Author Orcid Image ;   Jae Won Moon1 Author Orcid Image ;   Kwangsoo Kim2, 4 Author Orcid Image ;   Dong In Suh1 Author Orcid Image
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Journals

  1. Lyu Y, Jiang Q, Yuan S, Hong J, Chen C, Wu H, Wang Y, Shi Y, Yan H, Xu J. Non-invasive acoustic classification of adult asthma using an XGBoost model with vocal biomarkers. Scientific Reports 2025;15(1) View
  2. Sreejith R, Ramasamy R, Mohd-Isa W, Abdullah J. Enhanced Lung Disease Classification Using CALMNet: A Hybrid CNN-LSTM-TimeDistributed Model for Respiratory Sound Analysis. IEEE Access 2025;13:135053 View
  3. Lee J, Park S, Park J, Suh D, Kim K. LUNAR: Periodicity-aware time-series analysis framework for LUNg Auscultation Respiratory detection. Computers in Biology and Medicine 2025;197:110947 View
  4. Li S, Yang D, Song J, Wang Y. Innovations in pediatric lung health amid global health shifts. World Journal of Pediatrics 2025 View
  5. FUJITA W, SAKAMOTO A, SATO E, KANEKO T, KAGIYAMA N. Transformative Impact of Artificial Intelligence on Internal Medicine: Current Applications, Challenges, and Future Horizons for Urban Health. Juntendo Medical Journal 2025 View