Artificial intelligence has made a significant leap in its potential applications, as a new study published in The Journals of Gerontology suggests that AI might accurately reveal the aging pace of the human body through the analysis of chest X-rays. Researchers have developed a deep learning model, termed CXR-Age, which is able to identify nuanced, age-related changes in vital organs such as the heart and lungs more effectively than traditional DNA-based “epigenetic clocks.”
The study, titled “Deep learning chest X-ray age, epigenetic aging clocks, and associations with age-related subclinical disease in the Project Baseline Health Study,” compared the AI model against two well-established biological age measures derived from DNA methylation: Horvath Age and DNAm PhenoAge. The research team analyzed data from 2,097 adults participating in the Project Baseline Health Study, a comprehensive U.S. initiative aimed at enhancing the understanding of health and disease progression over time.
Findings from the study revealed that CXR-Age exhibited strong correlations with early indicators of cardiopulmonary aging, including coronary calcium, declining lung function, increased frailty, and higher levels of proteins associated with neuroinflammation and aging. In stark contrast, the DNA-based clocks displayed weaker or no associations, particularly among middle-aged participants.
“These findings suggest that deep learning applied to common medical images can reveal how our organs are aging—information that might one day help clinicians identify people at risk of age-related disease before symptoms develop,” stated Douglas P. Kiel, MD, MPH, director of the Musculoskeletal Research Center at the Hinda and Arthur Marcus Institute for Aging Research, and a co-author of the study. “AI tools like this could become an important complement to traditional risk assessments.”
The researchers concluded that the AI-derived CXR-Age may serve as a superior indicator of cardiopulmonary aging in comparison to existing epigenetic aging clocks. This highlights the potential of integrating medical imaging and machine learning into the development of personalized and preventive healthcare strategies.
The implications of this research could be far-reaching, paving the way for more accessible and non-invasive methods of assessing health risks linked to aging. As AI technology continues to evolve, it may not only enhance diagnostic accuracy but also transform the broader landscape of geriatric and preventive medicine.
For more information on the study, you can access the original publication with the DOI: 10.1093/gerona/glaf173.
Further details on the Project Baseline Health Study can be found on its official site.
For those interested in the development of innovative AI technologies, check the advancements at Nvidia.
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