ArticleRadiology2020
Deep Learning Enables Automatic Classification of Emphysema Pattern at CT.
Article in Radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 2 of them syntheses that pooled it.
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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
62 citing papers in PubMed, 2 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
- The Use of Computed Tomography Densitometry for the Assessment of Emphysema in Clinical Trials: A Position Paper from the Fleischner Society.American journal of respiratory and critical care medicine · 2025Pooled it
- Chest CT imaging toward personalized management of chronic obstructive pulmonary disease, asthma, and bronchiectasis: pulmonologist perspectives.Japanese journal of radiology · 2026Review
- Precision treatment of COPD based on novel imaging phenotypes: a treatable traits approach.Respiratory research · 2026Review
- Optimizing surgical decision-making for lung adenocarcinoma by preoperatively identifying pathological high-risk factors: retrospective development and prospective validation of a deep learning model.International journal of surgery (London, England) · 2026Article
- Association of automated quantified emphysema and interstitial lung abnormality with survival in non-small cell lung cancer.Insights into imaging · 2026Article
- Artificial Intelligence-Powered Chronic Obstructive Pulmonary Disease Detection Techniques-A Review.Diagnostics (Basel, Switzerland) · 2025Review
- Uncertainty-aware quantitative CT evaluation of emphysema and mortality risk from variable radiation dose images.European radiology · 2025Article
- Macrotrabecular-massive subtype in hepatocellular carcinoma based on contrast-enhanced CT: deep learning outperforms machine learning.Insights into imaging · 2025Article
- A robust chronic obstructive pulmonary disease classification model using dragonfly optimized kernel extreme learning machine.Scientific reports · 2025Article
- A method for quantifying and automatic grading of musculoskeletal ultrasound superb microvascular imaging based on dynamic analysis of optical flow model.Scientific reports · 2025Article
- Application of Artificial Intelligence in Thoracic Radiology: A Narrative Review.Tuberculosis and respiratory diseases · 2025Article
- Novel Lobe-based Transformer model (LobTe) to predict emphysema progression in Alpha-1 Antitrypsin Deficiency.Computers in biology and medicine · 2025Article
- A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging.Journal of imaging · 2024Article
- Explainable Encoder-Prediction-Reconstruction Framework for the Prediction of Metasurface Absorption Spectra.Nanomaterials (Basel, Switzerland) · 2024Article
- Artificial intelligence in COPD CT images: identification, staging, and quantitation.Respiratory research · 2024Review
- COPD: artificial intelligence detects and quantifies anomalies on chest CT enabling prediction of disease severity.European radiology · 2024Article
- [Using Artificial Intelligence Software for Diagnosing Emphysema and Interstitial Lung Disease].Journal of the Korean Society of Radiology · 2024Review
- Evaluation of a multiparametric renal CT algorithm for diagnosis of clear-cell renal cell carcinoma among small (≤ 4 cm) solid renal masses.European radiology · 2024Article
- Prognostication of lung adenocarcinomas using CT-based deep learning of morphological and histopathological features: a retrospective dual-institutional study.European radiology · 2024Article
2 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
BackgroundPattern of emphysema at chest CT, scored visually by using the Fleischner Society system, is associated with physiologic impairment and mortality risk.PurposeTo determine whether participant-level emphysema pattern could predict impairment and mortality when classified by using a deep learning method.Materials and MethodsThis retrospective analysis of Genetic Epidemiology of COPD (COPDGene) study participants enrolled between 2007 and 2011 included those with baseline CT, visual emphysema scores, and survival data through 2018. Participants were partitioned into nonoverlapping sets of 2407 for algorithm training, 100 for validation and parameter tuning, and 7143 for testing. A deep learning algorithm using convolutional neural network and long short-term memory architectures was trained to classify pattern of emphysema according to Fleischner criteria. Deep learning scores were compared with visual scores and clinical parameters including pulmonary function tests. Cox proportional hazard models were used to evaluate relationships between emphysema scores and survival. The algorithm was also tested by using CT and clinical data in 1962 participants enrolled in the Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE) study.ResultsA total of 7143 COPDGene participants (mean age ± standard deviation, 59.8 years ± 8.9; 3734 men and 3409 women) were evaluated. Deep learning emphysema classifications were associated with impaired pulmonary function tests, 6-minute walk distance, and St George's Respiratory Questionnaire at univariate analysis (
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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.