Editorial Volume 15 Issue 3 - 2026

Machine Learning in Pediatric Chronic Respiratory Diseases: Beyond Asthma

Alberto Vidal*

Unit of Pediatric Pulmonology, Clínica MEDS, Santiago, Chile

*Corresponding Author: Alberto Vidal, Unit of Pediatric Pulmonology, Clínica MEDS, Santiago, Chile.
Received: February 02, 2026; Published: February 12, 2026



Machine learning (ML) is an artificial intelligence (AI) tool that learns as it is fed data to create predictive algorithms. In medicine, it is increasingly used to predict future events that could be anticipated by preventing health-related risk factors. Until now, most medical AI research has focused on adults. Within pediatric subspecialties, pediatric pulmonology accounts for a smaller percentage of machine learning studies, and given its high prevalence, most of these have focused on asthma. With the continued development of AI, more pediatric respiratory diseases have been incorporated into ML, which also deserve discussion.

  1. Gill ER., et al. “Symptom phenotyping in people with cystic fibrosis during acute pulmonary exacerbations using machine-learning K-means clustering analysis”. Journal of Cystic Fibrosis 6 (2024): 1106-1111.
  2. Gill ER., et al. “Predicting return of lung function after a pulmonary exacerbation using the cystic fibrosis respiratory symptom diary-chronic respiratory infection symptom scale”. BMC Pulmonary Medicine 1 (2024): 360.
  3. Mazurek H., et al. “AI-facilitated home monitoring for cystic fibrosis exacerbations across pediatric and adult populations”. Journal of Cystic Fibrosis 2 (2025): 390-397.
  4. Filipow N., et al. “Unsupervised phenotypic clustering for determining clinical status in children with cystic fibrosis”. European Respiratory Journal 2 (2021): 2002881.
  5. Bianchim, M. S.., et al. “A machine learning approach for physical activity recognition in cystic fibrosis”. Measurement in Physical Education and Exercise Science 2 (2024): 172-181.
  6. Hadas N., et al. “Machine learning analysis of cilia-driven particle transport distinguishes primary ciliary dyskinesia cilia from normal cilia”. bioRxiv [Preprint] (2025): 11.02.686130.
  7. Burns G., et al. “Feasibility of machine learning analysis for the identification of patients with possible primary ciliary dyskinesia”. Orphanet Journal of Rare Diseases 1 (2025): 516.
  8. Moeller AL., et al. “Artificial intelligence or sleep experts: comparing polysomnographic sleep staging in children and adolescents”. Sleep7 (2025): zsaf053.
  9. Calderón JM., et al. “Development of a minimally invasive screening tool to identify obese pediatric population at risk of obstructive sleep apnea/hypopnea syndrome”. Bioengineering (Basel)4 (2020): 131.
  10. Xu W., et al. “Pediatric pulmonary function assessment using artificial intelligence with cough sounds”. Indian Journal of Pediatrics 8 (2024): 857-858.

Alberto Vidal. “Machine Learning in Pediatric Chronic Respiratory Diseases: Beyond Asthma”. EC Pulmonology and Respiratory Medicine  15.3 (2026): 01-03.