AI tools to improve pre-transplant kidney biopsy readings
Official title: Optimizing Preimplantation Kidney Transplant Biopsy Interpretation with Artificial Intelligence Assistance - Resubmission - 1
Funded at New York University School of Medicine · NIH-11369502 · Data updated 2026-11-12
This project will use artificial intelligence to help doctors read donor kidney biopsy images so more usable kidneys reach people on the transplant waitlist.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | New York University School of Medicine NIH-funded |
| Lab location | 1 site (New York, United States) |
| Project start | September 5, 2024 |
| Project end | June 30, 2029 |
| Funding year | 2025 |
| Award (this year) | $157,276 |
| NIH project number | 3R01DK138067-02S1 |
| Project ID | NIH-11369502 on NIH RePORTER |
Who could benefit from this research
✓Could help
People who are candidates for deceased-donor kidney transplantation or who are currently on a kidney transplant waitlist would be most directly affected.
✕Less likely to help
Patients receiving living-donor transplants or those treated at centers that do not use preimplantation biopsies are unlikely to see direct benefit from this project.
What this research studies
From my perspective, researchers will train a new AI on many digitized preimplantation kidney biopsy images paired with expert pathologist interpretations so the AI can learn important tissue patterns. They will compare AI-assisted readings with usual on-call pathologist readings and expert reviews to see if the AI can provide expert-level guidance in real time. The team plans to deploy the AI into clinical workflows at transplant centers where digital imaging is available, aiming to support decisions made overnight or when an expert is not immediately available. Ultimately they will track whether AI guidance changes discard decisions and post-transplant outcomes.
Why it matters
Potential benefit: If successful, this could reduce unnecessary discards of donor kidneys and help more people receive transplants sooner.
How similar studies have gone: AI has shown promise in other pathology tasks, but applying AI specifically to preimplantation kidney biopsy interpretation is a newer approach that is not yet proven.
Where this research is happening
-
New York University School of Medicine
New York, United States Active
Researchers
- Principal investigator: Segev, Dorry L. — New York University School of Medicine
- Study coordinator: Segev, Dorry L.
Related research
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Complement-Bound Exosomes and Kidney Transplant Immunity
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Understanding how BK virus links infection to kidney transplant rejection
This project looks at how BK polyomavirus reactivation affects kidney transplant patients and aims to use pre-transplant T cell information to tailor immunosuppression and monitoring.
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Characterization of a LILRB3 SNP cluster and its link to kidney transplant outcomes
A study in kidney transplant recipients looks at genetic factors that may be tied to higher risk of graft failure.
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.