ArticleInternational journal of chronic obstructive pulmonary disease2020
Prediction of Obstructive Lung Disease from Chest Radiographs via Deep Learning Trained on Pulmonary Function Data.
Article in International journal of chronic obstructive pulmonary disease, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 40 citations in OpenAlex.
- Feasibility of opportunistic screening for preserved ratio impaired spirometry using chest radiography-based deep learning models.European journal of radiology open · 2026Article
- Review of risk factors and prediction models for multidrug-resistant bacterial pulmonary infections in patients with chronic obstructive pulmonary disease.The Journal of international medical research · 2026Review
- Interpretable chronic obstructive pulmonary disease identification using chest X-ray radiomics: a multicenter study.Insights into imaging · 2026Article
- 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
- Clinical applications of large language models in medicine and surgery: A scoping review.The Journal of international medical research · 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
- Automated Lightweight Model for Asthma Detection Using Respiratory and Cough Sound Signals.Diagnostics (Basel, Switzerland) · 2025Article
- Utility of osteoporosis screening based on estimation of bone mineral density using bidirectional chest radiographs with deep learning models.Frontiers in medicine · 2025Article
- Explainable deep-neural-network supported scheme for tuberculosis detection from chest radiographs.BMC medical imaging · 2024Article
- Spirometry test values can be estimated from a single chest radiograph.Frontiers in medicine · 2024Article
- Quantifying the Preferential Direction of the Model Gradient in Adversarial Training With Projected Gradient Descent.Pattern recognition · 2023Article
- Effect of image resolution on automated classification of chest X-rays.Journal of medical imaging (Bellingham, Wash.) · 2023Article
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- Development of deep learning chest X-ray model for cardiac dose prediction in left-sided breast cancer radiotherapy.Scientific reports · 2022Article
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Chronic obstructive pulmonary disease (COPD), the third leading cause of death worldwide, is often underdiagnosed. Purpose: To develop machine learning methods to predict COPD using chest radiographs and a convolutional neural network (CNN) trained with near-concurrent pulmonary function test (PFT) data. Comparison is made to natural language processing (NLP) of the associated radiologist text reports. Materials and Methods: This IRB-approved single-institution retrospective study uses 6749 two-view chest radiograph exams (2012-2017, 4436 unique subjects, 54% female, 46% male), same-day associated radiologist text reports, and PFT exams acquired within 180 days. The Image Model (Resnet18 pre-trained with ImageNet CNN) is trained using frontal and lateral radiographs and PFTs with 10% of the subjects for validation and 19% for testing. The NLP Model is trained using radiologist text reports and PFTs. The primary metric of model comparison is the area under the receiver operating characteristic curve (AUC). Results: The Image Model achieves an AUC of 0.814 for prediction of obstructive lung disease (FEV1/FVC <0.7) from chest radiographs and performs better than the NLP Model (AUC 0.704, p<0.001) from radiologist text reports where FEV1 = forced expiratory volume in 1 second and FVC = forced vital capacity. The Image Model performs better for prediction of severe or very severe COPD (FEV1 <0.5) with an AUC of 0.837 versus the NLP model AUC of 0.770 (p<0.001). Conclusion: A CNN Image Model trained on physiologic lung function data (PFTs) can be applied to chest radiographs for quantitative prediction of obstructive lung disease with good accuracy.
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