Predicting Respiratory Disease Mortality Risk Using Open-source AI on Chest Radiographs in an Asian Health Screening Population.

Abstract

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To assess the prognostic value of an open-source deep learning-based chest radiographs (CXR) algorithm, CXR-Lung-Risk, for stratifying respiratory disease mortality risk among an Asian health screening population using baseline and follow-up CXRs. Materials and Methods This single-center, retrospective study analyzed CXRs from individuals who underwent health screenings between January 2004 and June 2018. The CXR-Lung-Risk scores from baseline CXRs were externally tested for predicting mortality due to lung disease or lung cancer, using competing risk analysis, with adjustments made for clinical factors. The additional value of these risk scores beyond clinical factors was evaluated using the likelihood ratio test. An exploratory analysis was conducted on the CXR-Lung-Risk trajectory over a three-year follow-up period for individuals in the highest quartile of baseline respiratory disease mortality risk, using a time-series clustering algorithm. Results Among 36,924 individuals (median age, 58 years [interquartile range: 53-62 years]; 22,352 male), 264 individuals (0.7%) died of respiratory illness, over a median follow-up period of 11.0 years (interquartile range: 7.8- 12.7 years). CXR-Lung-Risk predicted respiratory disease mortality (adjusted hazard ratio [HR] per 5 years: 2.01, 95% CI: 1.76-2.39, P < .001), offering a prognostic improvement over clinical factors (P < .001). The trajectory analysis identified a subgroup with a continuous increase in CXR-Lung-Risk, which was associated with poorer outcomes (adjusted HR for respiratory disease mortality: 3.26, 95% CI: 1.20-8.81, P = .02) compared with the subgroup with a continuous decrease in CXR-Lung-Risk. Conclusion The open-source CXR-Lung-Risk model predicted respiratory disease mortality in an Asian cohort, enabling a two-layer risk stratification approach through an exploratory longitudinal analysis of baseline and follow-up CXRs. ©RSNA, 2025.