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

2026/04/01 by Yoosoo Chang, Hyungjin Kim, Seungho Lee +3 · 1 voice · 1 citation
Medicine · #Accelerated aging #Artificial Intelligence in Healthcare and Education #Asymptomatic #COVID-19 diagnosis using AI #Cohort #Cohort study #Hazard ratio #Mortality rate #Poisson regression #Proportional hazards model #Radiography #Radiomics and Machine Learning in Medical Imaging

paper · doi:10.1148/ryai.250609

published in Radiology Artificial Intelligence 8(3), e250609 (Radiological Society of North America)

openalex publication_date 2026/04/01 · openalex created_date 2026/04/02 · openalex updated_date 2026/07/22

Abstract

Accelerated aging and aging velocity calculated from deep learning–based chest radiograph–derived biologic age were independently associated with all-cause, cardiovascular, cancer, and respiratory mortality in Korean adults undergoing regular health check-ups.

Citations

• Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Cited by

• Beyond the Age Gap: Longitudinal Aging Velocity as a Dynamic Biomarker in Chest Radiography

Discussions

• Deep Learning–based determination of age from chest X-rays predicts all-cause and cause-specific mortality https://doi.org/10.1148/ryai.250609 #OHBM2026 #NeuroRad #brain #radiology [bsky, 4 points, 0 comments]

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