New AI model analyses X-rays to predict ageing and disease risk
Machine learning and medical imaging offer new tools for personalised, preventive medicine by revealing how organs age over time.
AI may offer a new way to assess how quickly the body is aging by analysing chest X-rays, according to research published in The Journals of Gerontology. The CXR-Age AI model detected age-related changes in the heart, lungs, and health more accurately than DNA-based epigenetic clocks.
Researchers compared CXR-Age to two biological age measures, Horvath Age and DNAm PhenoAge, using data from 2,097 adults in the Project Baseline Health Study, a US multi-site initiative exploring health and disease over time.
CXR-Age showed strong links with early signs of heart and lung aging, frailty, and proteins associated with neuroinflammation, while DNA clocks displayed weaker or no correlations, particularly in middle-aged adults.
Findings suggest that AI applied to routine medical imaging could help clinicians identify individuals at risk of age-related diseases before symptoms appear. AI metrics like CXR-Age could enhance traditional assessments and support personalised preventive healthcare.
The study concludes that machine learning and medical imaging have the potential to advance understanding of organ-specific aging, offering a promising tool for monitoring cardiopulmonary health and supporting early interventions.
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