Accelerated Aging and Aging Velocity from Deep Learning–based Chest Radiograph–derived Age for Predicting Cause-specific Mortality

Purpose To assess the prognostic value of deep learning-derived radiographic age and aging velocity for predicting mortality in an Asian cohort. Materials and Methods This retrospective cohort study included Korean adults who underwent posteroanterior chest radiography between January 2006 and December 2020. Radiographic age was estimated using AgeNet, a deep learning model trained on the data of healthy asymptomatic individuals. Accelerated aging was defined as radiographic age exceeding chronological age by ≥ 5 years, and aging velocity as the annual change in radiographic age from serial radiographs. Multivariable Cox and Fine-Gray models were used to estimate adjusted hazard ratios (HRs) for all-cause and cause-specific mortality. Mortality rate ratios were estimated using Poisson regression. Results A total of 421,894 adults were included (mean age 47.4 years ± 8.8 SD; 227,427 53.9% male). During a median follow-up of 8.5 years, 6,506 deaths occurred (953 cardiovascular, 3,024 cancer, 1,043 respiratory). Accelerated aging was associated with increased all-cause and cause-specific mortality, with stronger associations in females (P-interaction = 0.008); the HRs for all-cause mortality were 1.26 (P P P P =.18; females, 0.50, P P <.001). Conclusion Radiographic age-based accelerated aging and aging velocity independently predicted all-cause and cause-specific mortality. © RSNA, 2026.