External Testing of a Deep Learning Model to Estimate Biological Age Using Chest Radiographs.

"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 a deep learning-based chest radiographic age (CXR-Age) model in a large external test cohort of Asian individuals. Materials and Methods This single-center, retrospective study included chest radiographs from consecutive, asymptomatic Asian individuals aged 50 to 80 who received health check-ups between January 2004 and June 2018. This study performed a dedicated external test of the previously developed CXR-Age model, which predicts an age adjusted based on the risk of all-cause mortality. Adjusted hazard ratios (HRs) of CXR-Age for all-cause, cardiovascular, lung cancer, and respiratory disease mortality were assessed using multivariable Cox or Fine-Gray models, with their added values evaluated by likelihood ratio tests. Results A total of 36,924 individuals (mean chronological age ± SD, 58 ± 7 years; CXR-Age, 60 ± 5 years; 22,352 male) were included. Over a median follow-up of 11.0 years, 1250 individuals (3.4%) died, including 153 cardiovascular (0.4%), 166 lung cancer (0.4%), and 98 respiratory deaths (0.3%). CXR-Age was a significant risk factor for all-cause (adjusted HR at the chronological age of 50 years: 1.03; at 60 years: 1.05; at 70 years: 1.07), cardiovascular (adjusted HR: 1.11), lung cancer (adjusted HR for former smokers: 1.12; for current smokers: 1.05), and respiratory disease mortality (adjusted HR: 1.12) (all P values < 0.05). The likelihood ratio test demonstrated added prognostic value of CXR-Age to clinical factors including chronological age for all outcomes (all P values < 0.001). Conclusion Deep learning-based chest radiographic age was associated with various survival outcomes and had added value to clinical factors in asymptomatic Asian individuals, suggesting its generalizability. ©RSNA, 2024.