AI detection of tiny kidney changes and protein signals that predict chronic kidney disease
Automated detection of microstructural features that have unique protein markers and are prognostic for chronic kidney disease
This project uses artificial intelligence to find tiny structural changes and protein markers in kidney tissue to help predict who may later develop chronic kidney disease.
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 ID | NIH-11321535 on NIH RePORTER |
What this research studies
Researchers will use deep learning to analyze digitized microscope images of donated and surgically removed kidneys to measure microscopic features like arteriosclerosis, glomerulosclerosis, nephron number and size, and podocyte density. They will pair those image-based measurements with targeted proteomic testing of the exact microstructures to find proteins that differ between kidneys that do or do not later develop CKD. The work uses existing samples and long-term kidney function data from living donors and nephrectomy patients within a multi-site Aging Kidney Anatomy resource. Combining automated image analysis with microstructure-specific protein data aims to reveal early signs and biological mechanisms that predict kidney decline.
Who could benefit from this research
Good fit: Ideal candidates are people who can provide kidney tissue (for example living kidney donors or patients undergoing nephrectomy) with linked long-term kidney function follow-up.
Not a fit: People without available kidney tissue samples or those with advanced, late-stage CKD are unlikely to directly benefit from this work.
Why it matters
Potential benefit: If successful, this could enable earlier prediction of chronic kidney disease and point to new targets for prevention or treatment.
How similar studies have performed: Previous research has used AI to measure kidney structures and proteomics to find disease markers, but combining automated microstructure morphometry with microstructure-specific proteomics for CKD prediction is relatively new.
Where this research is happening
Rochester, United States
- Mayo Clinic Rochester — Rochester, United States (Active)
Researchers
- Principal investigator: Rule, Andrew David — Mayo Clinic Rochester
- Study coordinator: Rule, Andrew David
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 below.