Chronological age has traditionally informed healthcare managers’ decisions in patient treatment, but it overlooks factors like genetics, lifestyle, and environment affecting an individual’s aging. To address this, scientists have created “biological age clocks,” such as epigenetic clocks, which assess biological age more accurately. These clocks, including DNAm PhenoAge and CXR-Age, can highlight earlier disease markers. CXR-Age utilizes chest X-ray imaging and deep learning to link aging with cardiovascular and lung health, showing a clear correlation with heart risks and lung function declines. Research indicates every year of increased CXR-Age correlates with significant deterioration in health metrics, independent of smoking status. This innovative model suggests that routine imaging could help preemptively identify age-related health risks, demonstrating a strong potential for integrating AI in medical imaging. As findings advance, these tools may enhance disease risk assessments, offering a proactive approach to aging-related healthcare.
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