ArticleMedical physics2021
Improving clinical disease subtyping and future events prediction through a chest CT-based deep learning approach.
Article in Medical physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Community-based prediction models of cardiovascular events, acute exacerbations and all-cause mortality in individuals with chronic obstructive pulmonary disease: a systematic review and meta-analysis on behalf of the International Cardiovascular and Respiratory Alliance.BMJ open respiratory research · 2026Pooled it
- Quantitative computed tomography and predictive modelling for COPD exacerbations.European respiratory review : an official journal of the European Respiratory Society · 2025Pooled it
- The role of artificial intelligence in predicting COPD exacerbations using multimodal data: a systematic review.ERJ open research · 2026Article
- Artificial Intelligence in Asthma and COPD: Current Status and Future Potential.Journal of clinical medicine · 2026Review
- Clinically aligned COPD severity prediction using ordinal neural networks.Frontiers in medicine · 2026Article
- Application of Artificial Intelligence in Thoracic Radiology: A Narrative Review.Tuberculosis and respiratory diseases · 2025Article
- Artificial intelligence in COPD CT images: identification, staging, and quantitation.Respiratory research · 2024Review
- How do deep-learning models generalize across populations? Cross-ethnicity generalization of COPD detection.Insights into imaging · 2024Article
- COPD: artificial intelligence detects and quantifies anomalies on chest CT enabling prediction of disease severity.European radiology · 2024Article
- Prediction of disease severity in COPD: a deep learning approach for anomaly-based quantitative assessment of chest CT.European radiology · 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
- Capturing COPD heterogeneity: anomaly detection and parametric response mapping comparison for phenotyping on chest computed tomography.Frontiers in medicine · 2024Article
- Machine learning slice-wise whole-lung CT emphysema score correlates with airway obstruction.European radiology · 2024Article
- Diagnostic efficacy of visual subtypes and low attenuation area based on HRCT in the diagnosis of COPD.BMC pulmonary medicine · 2022Article
- Deep-Learning-Based Survival Prediction of Patients in Coronary Care Units.Computational and mathematical methods in medicine · 2021Article
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Authors and funding
5 authors.
Funding
Abstract
purposeTo develop and evaluate a deep learning (DL) approach to extract rich information from high-resolution computed tomography (HRCT) of patients with chronic obstructive pulmonary disease (COPD).
methodsWe develop a DL-based model to learn a compact representation of a subject, which is predictive of COPD physiologic severity and other outcomes. Our DL model learned: (a) to extract informative regional image features from HRCT; (b) to adaptively weight these features and form an aggregate patient representation; and finally, (c) to predict several COPD outcomes. The adaptive weights correspond to the regional lung contribution to the disease. We evaluate the model on 10 300 participants from the COPDGene cohort.
resultsOur model was strongly predictive of spirometric obstruction (
conclusionsOur model independently predicted spirometric obstruction, emphysema severity, exacerbation risk, and mortality from CT imaging alone. This method has potential applicability in both research and clinical practice.
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