ArticleRadiology. Cardiothoracic imaging2021
Automated CT Staging of Chronic Obstructive Pulmonary Disease Severity for Predicting Disease Progression and Mortality with a Deep Learning Convolutional Neural Network.
Article in Radiology. Cardiothoracic imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 30 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed, 4 syntheses or guidelines pooled it, 54 citations in OpenAlex.
- Estimation of mortality hazard ratios by COPD grade using a systematic review and network meta-analysis: deriving grade-, sex-, and age-specific mortality for a German COPD decision model.Scientific reports · 2026Pooled 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
- A systematic review of artificial intelligence tools for chronic pulmonary embolism on CT pulmonary angiography.Frontiers in radiology · 2024Pooled it
- Artificial Intelligence in Asthma and COPD: Current Status and Future Potential.Journal of clinical medicine · 2026Review
- Ecological partitioning enables phage-antibiotic cooperation in a human Pseudomonas infection.Nature communications · 2026Article
- Artificial Intelligence Applications in Chronic Obstructive Pulmonary Disease: A Global Scoping Review of Diagnostic, Symptom-Based, and Outcome Prediction Approaches.Biomedicines · 2025Review
- Clinical Implementation of Inspiratory-Expiratory Chest CT: Defining Quality Criteria for Diagnostic Quality and Detection of Concurrent FEVRadiology. Cardiothoracic imaging · 2025Article
- Super-resolution deep learning reconstruction to evaluate lumbar spinal stenosis status on magnetic resonance myelography.Japanese journal of radiology · 2025Article
- A robust chronic obstructive pulmonary disease classification model using dragonfly optimized kernel extreme learning machine.Scientific reports · 2025Article
- Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: a comprehensive review of machine learning and deep learning approaches.European journal of medical research · 2025Review
- Large multimodality model fine-tuned for detecting breast and esophageal carcinomas on CT: a preliminary study.Japanese journal of radiology · 2025Article
- Advances in artificial intelligence applications for the management of chronic obstructive pulmonary disease.Frontiers in medicine · 2025Review
- Evaluating the Cumulative Benefit of Inspiratory CT, Expiratory CT, and Clinical Data for COPD Diagnosis and Staging through Deep Learning.Radiology. Cardiothoracic imaging · 2024Observational
- Article
- 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
- Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging.Diagnostics (Basel, Switzerland) · 2024Review
- Integrating Artificial Intelligence in the Diagnosis of COPD Globally: A Way Forward.Chronic obstructive pulmonary diseases (Miami, Fla.) · 2024Article
- Machine learning slice-wise whole-lung CT emphysema score correlates with airway obstruction.European radiology · 2024Article
Corrections and comments
- Commented on by
- Erratum issued
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
purposeTo develop a deep learning-based algorithm to stage the severity of chronic obstructive pulmonary disease (COPD) through quantification of emphysema and air trapping on CT images and to assess the ability of the proposed stages to prognosticate 5-year progression and mortality. MATERIALS AND
methodsIn this retrospective study, an algorithm using co-registration and lung segmentation was developed in-house to automate quantification of emphysema and air trapping from inspiratory and expiratory CT images. The algorithm was then tested in a separate group of 8951 patients from the COPD Genetic Epidemiology study (date range, 2007-2017). With measurements of emphysema and air trapping, bivariable thresholds were determined to define CT stages of severity (mild, moderate, severe, and very severe) and were evaluated for their ability to prognosticate disease progression and mortality using logistic regression and Cox regression.
resultsOn the basis of CT stages, the odds of disease progression were greatest among patients with very severe disease (odds ratio [OR], 2.67; 95% CI: 2.02, 3.53;
conclusionAutomated CT algorithms can facilitate staging of COPD severity, have diagnostic performance comparable with that of spirometric GOLD staging, and provide further prognostic value when used in conjunction with GOLD staging.
Identifiers
What OpenQuestion holds
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.