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

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;21(12):1196 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