Machine learning-enhanced HRCT analysis for diagnosis and severity assessment in pediatric asthma.

Journal: Pediatric pulmonology
PMID: 39041906

Abstract

OBJECTIVES: Chest high-resolution computed tomography (HRCT) is conditionally recommended to rule out conditions that mimic or coexist with severe asthma in children. However, it may provide valuable insights into identifying structural airway changes in pediatric patients. This study aims to develop a machine learning-based chest HRCT image analysis model to aid pediatric pulmonologists in identifying features of severe asthma.

Authors

  • Maria De Filippo
    Pediatric Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy.
  • Salvatore Fasola
    Institute for Biomedical Research and Innovation (IRIB), National Research Council (CNR), Palermo, Italy.
  • Federica De Matteis
    Diagnostic Imaging Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy.
  • Maria Sole Prevedoni Gorone
    Radiology Unit-Diagnostic Imaging I, Department of Diagnostic Medicine, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
  • Lorenzo Preda
    Unit of Imaging and Radiotherapy, Department of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy.
  • Martina Votto
    Pediatric Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy.
  • Velia Malizia
    Institute for Biomedical Research and Innovation, National Research Council, 90146 Palermo, Italy.
  • Gian Luigi Marseglia
    Department of Pediatrics, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, Italy.
  • Stefania La Grutta
    Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy, Palermo, Italy.
  • Amelia Licari
    Pediatric Clinic, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, Italy. Electronic address: amelia.licari@unipv.it.

Keywords

External Resources

Popular Topics
Recent Journals