ArticleAmerican journal of respiratory and critical care medicine2018
Disease Staging and Prognosis in Smokers Using Deep Learning in Chest Computed Tomography.
Article in American journal of respiratory and critical care medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05316025 (Grenoble Cardiovascular Digital Health Data Observatory), which is not on this map. Cited by 122 papers, 2 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.
Grenoble Cardiovascular Digital Health Data Observatory
Who cites it
122 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics.European journal of nuclear medicine and molecular imaging · 2019Pooled it
- Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review.F1000Research · 2019Pooled it
- Artificial Intelligence in Asthma and COPD: Current Status and Future Potential.Journal of clinical medicine · 2026Review
- Artificial Intelligence for Early Detection and Prediction of Chronic Obstructive Pulmonary Disease Exacerbations.Healthcare (Basel, Switzerland) · 2026Review
- Multimodal data-driven multitask learning for enhanced identification and classification of chronic obstructive pulmonary disease: a retrospective study.Journal of global health · 2026Article
- Artificial Intelligence Applications in Chronic Obstructive Pulmonary Disease: A Global Scoping Review of Diagnostic, Symptom-Based, and Outcome Prediction Approaches.Biomedicines · 2025Review
- 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
- Deep learning based CT images for lung function prediction in patients with chronic obstructive pulmonary disease.BMC pulmonary medicine · 2025Article
- Artificial Intelligence-Powered Chronic Obstructive Pulmonary Disease Detection Techniques-A Review.Diagnostics (Basel, Switzerland) · 2025Review
- 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
- Oxidative Stress and Inflammation in Hypoxemic Respiratory Diseases and Their Comorbidities: Molecular Insights and Diagnostic Advances in Chronic Obstructive Pulmonary Disease and Sleep Apnea.Antioxidants (Basel, Switzerland) · 2025Review
- 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
- 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
- The effect of a post-bronchodilator FEVRespiratory research · 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
- Diagnosis and Severity Assessment of COPD Based on Machine Learning of Chest CT Images.International journal of chronic obstructive pulmonary disease · 2025Article
- Unveiling the Influence of AI on Advancements in Respiratory Care: Narrative Review.Interactive journal of medical research · 2024Review
- Evaluating the Cumulative Benefit of Inspiratory CT, Expiratory CT, and Clinical Data for COPD Diagnosis and Staging through Deep Learning.Radiology. Cardiothoracic imaging · 2024Observational
- Automated deep learning-based bone mineral density assessment for opportunistic osteoporosis screening using various CT protocols with multi-vendor scanners.Scientific reports · 2024Article
- [Pulmonary Digital Twins].Open respiratory archives · 2024Review
62 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
- Commented on by
- Commented on by
Authors and funding
10 authors.
Funding
Abstract
rationaleDeep learning is a powerful tool that may allow for improved outcome prediction.
objectivesTo determine if deep learning, specifically convolutional neural network (CNN) analysis, could detect and stage chronic obstructive pulmonary disease (COPD) and predict acute respiratory disease (ARD) events and mortality in smokers.
methodsA CNN was trained using computed tomography scans from 7,983 COPDGene participants and evaluated using 1,000 nonoverlapping COPDGene participants and 1,672 ECLIPSE participants. Logistic regression (C statistic and the Hosmer-Lemeshow test) was used to assess COPD diagnosis and ARD prediction. Cox regression (C index and the Greenwood-Nam-D'Agnostino test) was used to assess mortality. MEASUREMENTS AND MAIN
resultsIn COPDGene, the C statistic for the detection of COPD was 0.856. A total of 51.1% of participants in COPDGene were accurately staged and 74.95% were within one stage. In ECLIPSE, 29.4% were accurately staged and 74.6% were within one stage. In COPDGene and ECLIPSE, the C statistics for ARD events were 0.64 and 0.55, respectively, and the Hosmer-Lemeshow P values were 0.502 and 0.380, respectively, suggesting no evidence of poor calibration. In COPDGene and ECLIPSE, CNN predicted mortality with fair discrimination (C indices, 0.72 and 0.60, respectively), and without evidence of poor calibration (Greenwood-Nam-D'Agnostino P values, 0.307 and 0.331, respectively).
conclusionsA deep-learning approach that uses only computed tomography imaging data can identify those smokers who have COPD and predict who are most likely to have ARD events and those with the highest mortality. At a population level CNN analysis may be a powerful tool for risk assessment.
Indexed as
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.