ArticleThe British journal of radiology2022
COPD identification and grading based on deep learning of lung parenchyma and bronchial wall in chest CT images.
Article in The British journal of radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 44 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
- Accuracy of Deep Learning in Diagnosing Chronic Obstructive Pulmonary Disease: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Automatic Body Region Classification in CT Scans Using Deep Learning.Journal of imaging informatics in medicine · 2026Article
- Artificial Intelligence in Asthma and COPD: Current Status and Future Potential.Journal of clinical medicine · 2026Review
- Multimodal data-driven multitask learning for enhanced identification and classification of chronic obstructive pulmonary disease: a retrospective study.Journal of global health · 2026Article
- Prediction of chronic obstructive pulmonary disease based on multimodal data and deep learning.APL bioengineering · 2025Article
- Machine learning models using dual-phase CT radiomics for early detection of PRISm.Scientific reports · 2025Article
- Deep learning based CT images for lung function prediction in patients with chronic obstructive pulmonary disease.BMC pulmonary medicine · 2025Article
- A robust chronic obstructive pulmonary disease classification model using dragonfly optimized kernel extreme learning machine.Scientific reports · 2025Article
- Diagnosis and Severity Assessment of COPD Based on Machine Learning of Chest CT Images.International journal of chronic obstructive pulmonary disease · 2025Article
- Tracheal computed tomography radiomics model for prediction of the Omicron variant of severe acute respiratory syndrome coronavirus 2.Radiologie (Heidelberg, Germany) · 2024Article
- Artificial intelligence in COPD CT images: identification, staging, and quantitation.Respiratory research · 2024Review
- Development and application of a deep learning-based comprehensive early diagnostic model for chronic obstructive pulmonary disease.Respiratory research · 2024Article
- Screening and staging of chronic obstructive pulmonary disease with deep learning based on chest X-ray images and clinical parameters.BMC pulmonary medicine · 2024Article
- Comparison of deep-learning and radiomics-based machine-learning methods for the identification of chronic obstructive pulmonary disease on low-dose computed tomography images.Quantitative imaging in medicine and surgery · 2024Article
- Application and Prospects of Artificial Intelligence Technology in Early Screening of Chronic Obstructive Pulmonary Disease at Primary Healthcare Institutions in China.International journal of chronic obstructive pulmonary disease · 2024Review
- Association of CT phenotype with pulmonary function in patients with chronic obstructive pulmonary disease and influencing factors of prognosis.American journal of translational research · 2023Article
- Clinical application of deep learning and radiomics in hepatic disease imaging: a systematic scoping review.The British journal of radiology · 2022Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveChest CT can display the main pathogenic factors of chronic obstructive pulmonary disease (COPD), emphysema and airway wall remodeling. This study aims to establish deep convolutional neural network (CNN) models using these two imaging markers to diagnose and grade COPD.
methodsSubjects who underwent chest CT and pulmonary function test (PFT) from one hospital (
resultsIn the test cohort, CNN achieved an area under the curve of 0.899 (95%CI: 0.853-0.935) to determine the existence of COPD, and an accuracy of 81.7% (76.2-86.7%), which was significantly higher than the accuracy 68.1% (61.6%-74.2%) using quantitative CT method (
conclusionCNN can identify emphysema and airway wall remodeling on CT images to infer lung function and determine the existence and severity of COPD. It provides an alternative way to detect COPD using the extensively available chest CT. ADVANCES IN KNOWLEDGE: CNN can identify the main pathological changes of COPD (emphysema and airway wall remodeling) based on CT images, to infer lung function and determine the existence and severity of COPD. CNN reached an area under the curve of 0.853 to determine the existence of COPD in the external test cohort. The CNN approach provides an alternative and effective way for early detection of COPD using extensively used chest CT, as an important alternative to pulmonary function test.
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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.