ArticleScientific reports2021
A 3D-CNN model with CT-based parametric response mapping for classifying COPD subjects.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Chest CT in the evaluation of chronic obstructive pulmonary disease: recommendations of Asian Society of Thoracic Radiology.The British journal of radiology · 2026Guideline
- Accuracy of Deep Learning in Diagnosing Chronic Obstructive Pulmonary Disease: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Comprehensive application of artificial intelligence in preserved ratio impaired spirometry: A systematic literature review.PloS one · 2026Pooled it
- Multimodal data-driven multitask learning for enhanced identification and classification of chronic obstructive pulmonary disease: a retrospective study.Journal of global health · 2026Article
- Identification of imaging-based pulmonary and extrapulmonary treatable traits in COPD: a review.Frontiers in medicine · 2026Review
- Global trends and hotspots in AI applications for CT detection of chronic obstructive pulmonary disease: A bibliometric analysis from 2012 to 2024.Lasers in medical science · 2025Review
- Multiscale simulation of respiratory airflow using physiologically consistent geometry and boundary conditions in OpenFOAM.Computers in biology and medicine · 2025Article
- Light Convolutional Neural Network to Detect Chronic Obstructive Pulmonary Disease (COPDxNet): A Multicenter Model Development and External Validation Study.medRxiv : the preprint server for health sciences · 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
- Classifying chronic obstructive pulmonary disease status using computed tomography imaging and convolutional neural networks: comparison of model input image types and training data severity.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Development and validation of a deep learning-based automated computed tomography image segmentation and diagnostic model for infectious hydronephrosis: a retrospective multicentre cohort study.EClinicalMedicine · 2025Article
- Automated AI-based image analysis for quantification and prediction of interstitial lung disease in systemic sclerosis patients.Respiratory research · 2025Article
- Evaluating the Cumulative Benefit of Inspiratory CT, Expiratory CT, and Clinical Data for COPD Diagnosis and Staging through Deep Learning.Radiology. Cardiothoracic imaging · 2024Observational
- BranchLabelNet: Anatomical Human Airway Labeling Approach using a Dividing-and-Grouping Multi-Label Classification.Medical & biological engineering & computing · 2024Article
- Detection of chronic obstructive pulmonary disease with deep learning using inspiratory and expiratory chest computed tomography and clinical information.Journal of thoracic disease · 2024Article
- Artificial intelligence in COPD CT images: identification, staging, and quantitation.Respiratory research · 2024Review
- Topologic Parametric Response Mapping Identifies Tissue Subtypes Associated with Emphysema Progression.Academic radiology · 2024Article
- Deep learning parametric response mapping from inspiratory chest CT scans: a new approach for small airway disease screening.Respiratory research · 2023Article
- Development of machine learning model to predict pulmonary function with low-dose CT-derived parameter response mapping in a community-based chest screening cohort.Journal of applied clinical medical physics · 2023Article
- Radiomics for Improved Detection of Chronic Obstructive Pulmonary Disease in Low-Dose and Standard-Dose Chest CT Scans.Radiology · 2023Article
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10 authors.
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Abstract
Chronic obstructive pulmonary disease (COPD) is a respiratory disorder involving abnormalities of lung parenchymal morphology with different severities. COPD is assessed by pulmonary-function tests and computed tomography-based approaches. We introduce a new classification method for COPD grouping based on deep learning and a parametric-response mapping (PRM) method. We extracted parenchymal functional variables of functional small airway disease percentage (fSAD%) and emphysema percentage (Emph%) with an image registration technique, being provided as input parameters of 3D convolutional neural network (CNN). The integrated 3D-CNN and PRM (3D-cPRM) achieved a classification accuracy of 89.3% and a sensitivity of 88.3% in five-fold cross-validation. The prediction accuracy of the proposed 3D-cPRM exceeded those of the 2D model and traditional 3D CNNs with the same neural network, and was comparable to that of 2D pretrained PRM models. We then applied a gradient-weighted class activation mapping (Grad-CAM) that highlights the key features in the CNN learning process. Most of the class-discriminative regions appeared in the upper and middle lobes of the lung, consistent with the regions of elevated fSAD% and Emph% in COPD subjects. The 3D-cPRM successfully represented the parenchymal abnormalities in COPD and matched the CT-based diagnosis of COPD.
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