Open-Source AI Model for Predicting Respiratory Mortality in COPD from Chest Radiographs

Purpose To evaluate the clinical utility of artificial intelligence (AI) scores in years derived from chest radiographs (CXR-Lung-Risk scores) in predicting respiratory mortality in a cohort of patients with chronic obstructive pulmonary disease (COPD). Materials and Methods This retrospective single-center study included patients with COPD from a tertiary center between January 2011 and December 2015. CXR-Lung-Risk scores were derived from chest radiographs using an open-source AI algorithm. The primary outcome, respiratory mortality, was assessed for its association with CXR-Lung-Risk using a multivariable Fine-Gray model adjusted for age, sex, body mass index, smoking status, comorbidities, and pulmonary function test results. Discrimination was evaluated and benchmarked against the Global Initiative for Chronic Obstructive Lung Disease grade using the time-dependent area under the receiver operating characteristic curve (AUC). Associations between CXR-Lung-Risk and lung-function measures were examined. Results A total of 4226 patients (median age, 70 years [IQR, 63-76 years]; 3293 male) with COPD were evaluated. Respiratory mortality was observed in 19.7% (831 of 4226) of patients at a median follow-up of 6.7 years (IQR, 4.0-7.9 years). CXR-Lung-Risk was a prognostic factor for respiratory mortality (subdistribution hazard ratio per 5-year increase, 1.16; 95% CI: 1.10, 1.28; <i>P</i> < .001) after adjusting for lung function and clinical risk factors. Likelihood-ratio testing further confirmed its added value in multivariable models (<i>P</i> < .001). The AUC for CXR-Lung-Risk in predicting respiratory mortality up to 10 years was 0.76 (95% CI: 0.72, 0.79), which outperformed the Global Initiative for Chronic Obstructive Lung Disease grades (0.61; 95% CI: 0.58, 0.65; <i>P</i> < .001). Pulmonary function decreased with increasing CXR-Lung-Risk scores (<i>P</i> < .001). Conclusion This study demonstrates that CXR-Lung-Risk is a valuable open-source AI tool for predicting respiratory mortality among patients with COPD. <b>Keywords:</b> Chronic Obstructive Pulmonary Disease, Artificial Intelligence, Chest Radiograph, Prognostication <i>Supplemental material is available for this article.</i> © RSNA, 2025.