ArticleRespiratory research2023
Deep learning parametric response mapping from inspiratory chest CT scans: a new approach for small airway disease screening.
Article in Respiratory research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Chest CT in the evaluation of chronic obstructive pulmonary disease: recommendations of Asian Society of Thoracic Radiology.The British journal of radiology · 2026Guideline
- Chest CT imaging toward personalized management of chronic obstructive pulmonary disease, asthma, and bronchiectasis: pulmonologist perspectives.Japanese journal of radiology · 2026Review
- Feature-Based Parametric Response Mapping on Thoracic Computed Tomography for Robust Disease Classification in COPD.medRxiv : the preprint server for health sciences · 2026Article
- Article
- Comparative longitudinal analysis of pulmonary function post-pediatric Allo-HSCT: benign vs. malignant diseases and early predictors.Annals of hematology · 2025Article
- Utility of an artificial intelligence-based lung CT airway model in the quantitative evaluation of large and small airway lesions in patients with chronic obstructive pulmonary disease.BMC pulmonary medicine · 2025Article
- Do You Believe in Magic? Computed Tomography, Artificial Intelligence, and Chronic Obstructive Pulmonary Disease.American journal of respiratory and critical care medicine · 2025Article
- Deep Learning Estimation of Small Airway Disease from Inspiratory Chest Computed Tomography: Clinical Validation, Repeatability, and Associations with Adverse Clinical Outcomes in Chronic Obstructive Pulmonary Disease.American journal of respiratory and critical care medicine · 2025Article
- AutoCOPD-A novel and practical machine learning model for COPD detection using whole-lung inspiratory quantitative CT measurements: a retrospective, multicenter study.EClinicalMedicine · 2025Article
- Investigation of inert gas washout methods in a new numerical model based on an electrical analogy.Medical & biological engineering & computing · 2025Article
- Small airway disease as a key factor in COPD: new perspectives and insights.Frontiers in medicine · 2025Review
- Trends and Clinical Implications of Pediatric Pulmonary Function After Hematopoietic Stem Cell Transplantation: A Systematic Review.Health science reports · 2025Review
Corrections and comments
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Authors and funding
11 authors at 8 institutions in 3 countries.
Funding
Abstract
objectivesParametric response mapping (PRM) enables the evaluation of small airway disease (SAD) at the voxel level, but requires both inspiratory and expiratory chest CT scans. We hypothesize that deep learning PRM from inspiratory chest CT scans can effectively evaluate SAD in individuals with normal spirometry.
methodsWe included 537 participants with normal spirometry, a history of smoking or secondhand smoke exposure, and divided them into training, tuning, and test sets. A cascaded generative adversarial network generated expiratory CT from inspiratory CT, followed by a UNet-like network predicting PRM using real inspiratory CT and generated expiratory CT. The performance of the prediction is evaluated using SSIM, RMSE and dice coefficients. Pearson correlation evaluated the correlation between predicted and ground truth PRM. ROC curves evaluated predicted PRM
resultsOur method can generate expiratory CT of good quality (SSIM 0.86, RMSE 80.13 HU). The predicted PRM dice coefficients for normal lung, emphysema, and fSAD regions are 0.85, 0.63, and 0.51, respectively. The volume percentages of emphysema and fSAD showed good correlation between predicted and ground truth PRM (|r| were 0.97 and 0.64, respectively, p < 0.05). Predicted PRM
conclusionOur deep learning method generates high-quality PRM using inspiratory chest CT and effectively stratifies SAD in individuals with normal spirometry.
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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.