ArticlePloS one2021
Improved detection of air trapping on expiratory computed tomography using deep learning.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- State-of-the-art review of lung imaging in cystic fibrosis with recommendations for pulmonologists and radiologists from the "iMAging managEment of cySTic fibROsis" (MAESTRO) consortium.European respiratory review : an official journal of the European Respiratory Society · 2022Pooled it
- Segmentation-based deep learning emphysema quantification using chest CT: improved accuracy and robustness vs LAA-950.European radiology · 2026Article
- Feature-Based Parametric Response Mapping on Thoracic Computed Tomography for Robust Disease Classification in COPD.medRxiv : the preprint server for health sciences · 2026Article
- Radiomics and Artificial Intelligence in Pulmonary Fibrosis.Journal of imaging informatics in medicine · 2025Review
- Local heterogeneity of normal lung parenchyma and small airways disease are associated with COPD severity and progression.Respiratory research · 2024Article
- Quantitative CT of Normal Lung Parenchyma and Small Airways Disease Topologies are Associated With COPD Severity and Progression.medRxiv : the preprint server for health sciences · 2023Article
- Computer-assisted evaluation of small airway disease in CT scans of Iran-Iraq war victims of chemical warfare by a locally developed software: comparison between different quantitative methods.BMC medical imaging · 2023Article
- Implementation and evaluation of ultra-low dose CT in early cystic fibrosis lung disease.The European respiratory journal · 2023Article
- CT-based Machine Learning for Donor Lung Screening Prior to Transplantation.medRxiv : the preprint server for health sciences · 2023Article
- Artificial Intelligence Techniques to Predict the Airway Disorders Illness: A Systematic Review.Archives of computational methods in engineering : state of the art reviews · 2023Review
- Reader Perceptions and Impact of AI on CT Assessment of Air Trapping.Radiology. Artificial intelligence · 2022Article
Corrections and comments
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Authors and funding
16 authors at 5 institutions in 2 countries.
Funding
Abstract
backgroundRadiologic evidence of air trapping (AT) on expiratory computed tomography (CT) scans is associated with early pulmonary dysfunction in patients with cystic fibrosis (CF). However, standard techniques for quantitative assessment of AT are highly variable, resulting in limited efficacy for monitoring disease progression.
objectiveTo investigate the effectiveness of a convolutional neural network (CNN) model for quantifying and monitoring AT, and to compare it with other quantitative AT measures obtained from threshold-based techniques. MATERIALS AND
methodsPaired volumetric whole lung inspiratory and expiratory CT scans were obtained at four time points (0, 3, 12 and 24 months) on 36 subjects with mild CF lung disease. A densely connected CNN (DN) was trained using AT segmentation maps generated from a personalized threshold-based method (PTM). Quantitative AT (QAT) values, presented as the relative volume of AT over the lungs, from the DN approach were compared to QAT values from the PTM method. Radiographic assessment, spirometric measures, and clinical scores were correlated to the DN QAT values using a linear mixed effects model.
resultsQAT values from the DN were found to increase from 8.65% ± 1.38% to 21.38% ± 1.82%, respectively, over a two-year period. Comparison of CNN model results to intensity-based measures demonstrated a systematic drop in the Dice coefficient over time (decreased from 0.86 ± 0.03 to 0.45 ± 0.04). The trends observed in DN QAT values were consistent with clinical scores for AT, bronchiectasis, and mucus plugging. In addition, the DN approach was found to be less susceptible to variations in expiratory deflation levels than the threshold-based approach.
conclusionThe CNN model effectively delineated AT on expiratory CT scans, which provides an automated and objective approach for assessing and monitoring AT in CF patients.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.