ArticlePediatric pulmonology2024
Machine learning-enhanced HRCT analysis for diagnosis and severity assessment in pediatric asthma.
Article in Pediatric pulmonology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence-based models in predicting acute exacerbations and diagnosing pediatric asthma: a systematic review and meta-analysis.BMC medical informatics and decision making · 2026Pooled it
- The Evolving Role of Artificial Intelligence in Pediatric Asthma Management: Opportunities and Challenges for Modern Healthcare.Journal of personalized medicine · 2026Review
- Innovative diagnostic techniques and their clinical implications in food allergy: current clinical practice and future perspectives.Frontiers in allergy · 2026Review
- CT imaging in children with non-severe asthma and cough-variant asthma: functional spasm or structural remodeling?Frontiers in medicine · 2026Article
- Leveraging artificial intelligence for the management of preschool wheeze: A narrative review.Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology · 2025Review
- Machine learning-enhanced HRCT analysis for diagnosis and severity assessment in pediatric asthma.Pediatric pulmonology · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
objectivesChest 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.
methodsThis 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 imaging features that mark severe asthma from controls.
resultsChest HRCT scans differentiated children with severe asthma from controls. Compared to controls (n = 21, mean age 11.4 years), children with severe asthma (n = 20, mean age 10.4 years) showed significantly greater bronchial thickening (BT) scores (p < 0.001), airway wall thickness percentage (AWT%, p < 0.001), bronchiectasis grading (BG) and bronchiectasis severity (BS) scores (p = 0.016), mucus plugging, and centrilobular emphysema (p = 0.009). Using AWT% as the predictor in conventional ROC analysis, an AWT% ≥ 38.6 emerged as the optimal classifier for discriminating severe asthmatics from controls, with 95% sensitivity, specificity, and overall accuracy.
conclusionOur study demonstrates the potential of machine learning-based analysis of chest HRCT scans to accurately identify features associated with severe asthma in children, enhancing diagnostic evaluation and contributing to the development of more targeted treatment approaches.
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