Using CT scans to measure aging in the body
Official title: A population-based study of deep learning derived organ and tissue measures for accelerated aging using repurposed abdominal CT images
Funded at Mayo Clinic Rochester · NIH-11111427 · Data updated 2026-11-12
This study is looking at how advanced computer techniques can use your abdominal CT scans to see how your body's aging might differ from your actual age, helping to spot anyone who may be aging faster than usual and potentially at risk for health issues.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Mayo Clinic Rochester NIH-funded |
| Lab location | 1 site (Rochester, United States) |
| Project start | September 30, 2023 |
| Project end | May 31, 2028 |
| Funding year | 2025 |
| Award (this year) | $657,178 |
| NIH project number | 5R01AG081223-03 |
| Project ID | NIH-11111427 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are adults aged 65 and older who have undergone abdominal CT scans.
✕Less likely to help
Patients under the age of 65 or those who have not had abdominal CT scans may not benefit from this research.
What this research studies
This research investigates how deep learning techniques can analyze abdominal CT images to assess biological aging in individuals. By comparing biological age, which reflects changes in organs and tissues, to chronological age, the study aims to identify those who are aging faster than their peers. The approach utilizes existing CT scans, which are commonly performed, to derive quantitative measures of organ and tissue health. This could help predict risks for diseases and overall health outcomes before clinical symptoms appear.
Why it matters
Potential benefit: If successful, this research could lead to earlier identification of individuals at risk for age-related diseases, allowing for timely interventions.
How similar studies have gone: Previous research has shown promise in using imaging techniques to assess biological aging, but this specific application of deep learning to CT scans is relatively novel.
Where this research is happening
-
Mayo Clinic Rochester
Rochester, United States Active
Researchers
- Principal investigator: Rule, Andrew David — Mayo Clinic Rochester
- Study coordinator: Rule, Andrew David
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.