# Optimizing Preimplantation Kidney Transplant Biopsy Interpretation with Artificial Intelligence Assistance  - Resubmission - 1

> **NIH NIH R01** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2024 · $735,047

## Abstract

PROJECT SUMMARY
Less-than-ideal kidneys recovered from deceased donors continue to be discarded at a rate of 40-50%, but
evidence shows that many discarded kidneys could have yielded good outcomes and reduced the needlessly
high mortality on the transplant waitlist. Ensuring appropriate utilization and preventing discards of usable
kidneys requires expert pathologist interpretations of preimplantation biopsies, as such expert biopsy
interpretations strongly correlate with post-transplant outcomes above and beyond clinical characteristics.
However, organ offers often occur in the middle of the night, and many transplant centers lack 24/7 availability
of an experienced kidney transplant pathologist, so expert interpretations are often unavailable in real time.
Instead, clinical decision-making relies on the unreliable and inconsistent interpretations of on-call general
pathologists. Preimplantation biopsy interpretation from non-experts carries a high risk of inappropriate discard;
in fact, biopsy findings are cited as justification for nearly 40% of discards. We hypothesize that applying
modern artificial intelligence (AI) techniques, including a novel self-supervised deep learning framework that
we have recently developed called Histomorphological Phenotype Learning to identify histopathologic clusters,
could ensure universal access to reliable preimplantation biopsy interpretation and reduce discards. This is
feasible as digital imaging is becoming the standard amongst organ procurement organizations (OPOs), and
AI-assisted histopathological interpretation has proven superior in other clinical scenarios.
Leveraging an array of >10,000 biopsy images shared from 4 OPOs, and externally validated with images from
another 8 OPOs as well as an international cohort, we will compare self-supervised and expert-supervised “AI-
pathology assisted” (AIPA) biopsy interpretations to those of an on-call pathologist (standard of care). We will
also compare AIPA to existing kidney biopsy scoring systems. Further, we will study stakeholder attitudes and
catalog facilitators and barriers to implementation of AI-assisted preimplantation biopsy interpretation, to
encourage rapid clinical adaptation to improved biopsy interpretations. To improve the clinical utility of
preimplantation biopsies and reduce inappropriate organ discard, we propose the development AIPA with the
following aims: (1) to use self-supervised and expert-supervised learning to construct a comprehensive
unbiased atlas of histopathology in preimplantation deceased donor kidney biopsies, (2) to develop and
externally validate AIPA-derived models and compare interpretations and association with clinically relevant
post-transplant outcomes, and (3) to develop consensus for the implementation of AIPA in clinical practice.
Our findings will be immediately clinically useful to kidney transplant providers and patients across the US in
evaluating the 50,000 kidneys recovered for potential transplantation ...

## Key facts

- **NIH application ID:** 10980817
- **Project number:** 1R01DK138067-01A1
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Morgan Erika Grams
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $735,047
- **Award type:** 1
- **Project period:** 2024-09-05 → 2029-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10980817

## Citation

> US National Institutes of Health, RePORTER application 10980817, Optimizing Preimplantation Kidney Transplant Biopsy Interpretation with Artificial Intelligence Assistance  - Resubmission - 1 (1R01DK138067-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10980817. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*