Machine learning-based approaches for distinguishing viral and bacterial pneumonia in paediatrics: A scoping review.

Journal: Computer methods and programs in biomedicine
Published Date: Aug 1, 2025

Abstract

BACKGROUND AND OBJECTIVE: Pneumonia is the leading cause of hospitalisation and mortality among children under five, particularly in low-resource settings. Accurate differentiation between viral and bacterial pneumonia is essential for guiding appropriate treatment, yet it remains challenging due to overlapping clinical and radiographic features. Advances in machine learning (ML), particularly deep learning (DL), have shown promise in classifying pneumonia using chest X-ray (CXR) images. This scoping review summarises the evidence on ML techniques for classifying viral and bacterial pneumonia using CXR images in paediatric patients.

Authors

  • Declan Rickard
    School of Clinical Medicine, UNSW Sydney, Kensington, NSW, 2052, Australia. Electronic address: z5308458@ad.unsw.edu.au.
  • Muhammad Ashad Kabir
    School of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, NSW, 2795, Australia; Artificial Intelligence and Cyber Futures Institute, Charles Sturt University, Bathurst, NSW, 2795, Australia. Electronic address: akabir@csu.edu.au.
  • Nusrat Homaira
    School of Clinical Medicine, UNSW Sydney, Kensington, NSW, 2052, Australia; Discipline of Pediatrics and Child Health, UNSW Sydney, Randwick, NSW, 2031, Australia; Respiratory Department, Sydney Children's Hospital, Randwick, NSW, 2031, Australia. Electronic address: n.homaira@unsw.edu.au.

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