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

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.

Methods

This retrospective case-control study compared children with severe asthma (as defined by ERS/ATS guidelines) to age- and sex-matched controls without asthma, using chest HRCT scans for detailed imaging analysis. Statistical analysis included classification trees, random forests, and conventional ROC analysis to identify the most significant i

SUBMITTER: De Filippo M 

PROVIDER: S-EPMC11601025 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature