Open-Source AI Model for Predicting Respiratory Mortality in COPD from Chest Radiographs
Jong Hyuk Lee1, Chang-Hoon Lee2, Jayoun Kim3
1Department of Radiology, Seoul National University Hospital, Seoul, Korea.
Radiology. Cardiothoracic Imaging
|October 30, 2025
Summary
Artificial intelligence (AI) scores from chest radiographs (CXR-Lung-Risk) effectively predict respiratory mortality in chronic obstructive pulmonary disease (COPD) patients. This AI tool offers superior prognostication compared to existing grading systems.
Area of Science:
- Medical Imaging and Artificial Intelligence
- Pulmonary Medicine and Respiratory Diseases
Background:
- Chronic Obstructive Pulmonary Disease (COPD) poses a significant global health burden.
- Accurate prognostication is crucial for managing COPD patients and improving outcomes.
- Existing risk stratification methods may not fully capture disease severity or progression.
Purpose of the Study:
- To assess the clinical utility of artificial intelligence (AI) derived scores from chest radiographs (CXR-Lung-Risk) in predicting respiratory mortality.
- To evaluate the performance of CXR-Lung-Risk scores against the Global Initiative for Chronic Obstructive Lung Disease (GOLD) grading system.
Main Methods:
- Retrospective analysis of 4226 COPD patients from a tertiary center (2011-2015).
- CXR-Lung-Risk scores generated using an open-source AI algorithm applied to chest radiographs.
- Multivariable Fine-Gray models used to assess the association between CXR-Lung-Risk and respiratory mortality, adjusting for clinical factors and lung function; discrimination evaluated using AUC.
Main Results:
- CXR-Lung-Risk scores significantly predicted respiratory mortality (subdistribution hazard ratio per 5-year increase, 1.16; P < .001), demonstrating added value beyond lung function and clinical factors.
- The area under the receiver operating characteristic curve (AUC) for CXR-Lung-Risk was 0.76, outperforming GOLD grades (AUC = 0.61; P < .001) in predicting mortality up to 10 years.
- Higher CXR-Lung-Risk scores correlated with decreased pulmonary function (P < .001).
Conclusions:
- CXR-Lung-Risk, an open-source AI tool, is a valuable and effective prognosticator for respiratory mortality in COPD patients.
- This AI-driven approach offers improved risk stratification compared to traditional methods, potentially aiding clinical decision-making.
- The findings support the integration of AI-powered image analysis into routine COPD management.
Keywords:
Artificial IntelligenceChest RadiographChronic Obstructive Pulmonary DiseasePrognosticationMore Related Videos
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