AI-assisted review of donated kidney biopsy images
Official title: Optimizing Preimplantation Kidney Transplant Biopsy Interpretation with Artificial Intelligence Assistance - Resubmission - 1
Funded at New York University School of Medicine · NIH-11181564 · Data updated 2026-11-12
Using an AI tool to help pathologists read donor kidney biopsies so more usable deceased-donor kidneys can be transplanted for people on the 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) | $734,376 |
| NIH project number | 5R01DK138067-02 |
| Project ID | NIH-11181564 on NIH RePORTER |
Who could benefit from this research
✓Could help
People on the deceased-donor kidney transplant waitlist or those receiving an organ offer that relies on a preimplantation biopsy would be the primary group affected by this work.
✕Less likely to help
Patients receiving living-donor transplants or those not eligible for transplant would be unlikely to benefit directly from this project.
What this research studies
This project builds an AI system that reads digital images of preimplantation kidney biopsies to match expert pathologist interpretations. The team will train a novel self-supervised deep learning model to find histologic patterns linked to good transplant outcomes and compare its readings to on-call pathologist reports. The goal is to give transplant teams fast, reliable biopsy information any time of day so potentially good kidneys are not needlessly discarded. If successful, the AI would be deployed to support decision-making across transplant centers and reduce variability in biopsy interpretation.
Why it matters
Potential benefit: If successful, this could increase the number of usable deceased-donor kidneys offered for transplant and help shorten waitlist times and reduce deaths while waiting.
How similar studies have gone: Previous AI approaches in pathology have shown promise at reproducing expert interpretations, but applying a new self-supervised histomorphology method specifically to preimplantation kidney discard is relatively novel.
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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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.