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MINI REVIEW article

Front. Med.
Sec. Pulmonary Medicine
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1460050
This article is part of the Research Topic An Overview on Allergic and Pulmonary Diseases: from Birth to Childhood - Volume II View all articles

Artificial intelligence and wheezing in children: where are we now?

Provisionally accepted
Laura Venditto Laura Venditto 1,2Sonia Morano Sonia Morano 1Michele Piazza Michele Piazza 1*Marco Zaffanello Marco Zaffanello 1Laura Tenero Laura Tenero 3Giorgio Piacentini Giorgio Piacentini 1Giuliana Ferrante Giuliana Ferrante 1,4
  • 1 Department of Surgery, Dentistry, Pediatrics and Gynaecology, Pediatric Division, University of Verona, Verona, Italy
  • 2 Cystic Fibrosis Center of Verona, Azienda Ospedaliera Universitaria Integrata, Verona, Italy, Verona, Italy
  • 3 Pediatric Division, University Hospital of Verona, Verona, Italy, Verona, Italy
  • 4 Institute of Translational Pharmacology (IFT), National Research Council (CNR), Palermo, Italy, Palermo, Italy

The final, formatted version of the article will be published soon.

    Wheezing is a common condition in childhood, and its prevalence has increased in the last decade. Up to one-third of preschoolers develop recurrent wheezing, significantly impacting their quality of life and healthcare resources. Artificial Intelligence (AI) technologies have recently been applied in paediatric allergology and pulmonology, contributing to disease recognition, risk stratification, and decision support. Additionally, the COVID-19 pandemic has shaped healthcare systems, resulting in an increased workload and the necessity to reduce access to hospital facilities. In this view, AI and Machine Learning (ML) approaches can help address current issues in managing preschool wheezing, including its recognition, phenotyping, and asthma prediction, while improving parent-led/selfmanagement and reducing economic and social costs. This minireview aims to update our knowledge on recent advancements of AI applications in childhood wheezing, summarizing and discussing the current evidence in recognition, diagnosis, phenotyping, and asthma prediction, with an overview of home monitoring and tele-management.

    Keywords: wheezing, machine learning, artificial intelligence, Asthma, Digital Health

    Received: 05 Jul 2024; Accepted: 23 Jul 2024.

    Copyright: © 2024 Venditto, Morano, Piazza, Zaffanello, Tenero, Piacentini and Ferrante. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Michele Piazza, Department of Surgery, Dentistry, Pediatrics and Gynaecology, Pediatric Division, University of Verona, Verona, Italy

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.