# Computational Image Analysis of Renal Transplant Biopsies to Predict Graft Outcome

> **NIH NIH R01** · UNIVERSITY OF FLORIDA · 2024 · $532,457

## Abstract

Project Summary
Kidney transplantation is the most effective modality for treating end stage kidney disease. It provides superior
quality of life and significantly improves survival over dialysis. However, the demand for kidney transplants has
surpassed the supply of usable organs. Because of this deficit, it is important to improve the outcomes of first-
time transplant recipients through intelligent management, thereby optimizing donor organ allocation and
reducing the need for secondary transplants. In assessing the health of a renal allograft, time is of critical
importance. Being able to precisely predict delayed graft dysfunction and modifying treatment strategies
accordingly would be greatly impactful in decreasing chronic rejection events. Existing clinical methods, such as
the Kidney Donor Profile Index, which are based solely on donor demographics and clinical data, are minimally
to moderately predictive of allograft outcomes. Further, current visual, semi-quantitative transplant biopsy scoring
metrics, e.g., Banff, the Maryland Aggregate Pathology Index, and Remuzzi are often not predictive of renal graft
function. Digital image analytical methods that quantify chronic changes in kidney that cannot be done visually,
may offer clues to long-term allograft outcome. Therefore, to address the unmet need of intelligent renal
transplant management, we propose a comprehensive multimodal framework, integrating high-resolution renal
transplant biopsy digital whole-slide images (WSIs), and donor and recipient clinical, demographic, and social
determinants of health data. Using this framework we will combine computer vision and explainable artificial
intelligence (XAI) tools to derive autonomous diagnostic and prognostic models for data-driven, long-term
management of renal allografts. As part of their preliminary work, the investigator team has developed a
computational tool to quantify interstitial fibrosis and tubular atrophy, a chronicity measure in renal transplant
biopsies, and demonstrated that the prediction of estimated glomerular filtration rate at a later time-point after
biopsy using machine learning (ML)-derived image features outperforms those based on routine visual
assessment. This tool will be expanded to incorporate a variety of additional analyses including robust
segmentation of renal compartments in WSIs, leveraging pathologist guided attention to train deep-learning
models, state-of-the-art transformer models for multi-task learning, and XAI to increase interoperability and
accessibility of ML-derived predictions to pathologists. The performance of this pipeline to predict renal allograft
function in a future time-point will be compared with existing methods used in a clinical setting as well as ML-
based methods used for explainable prediction of disease progression in other areas of digital pathology. The
tool will be deployed on a cloud-based platform and the usability by important stakeholders, namely, transplant
renal p...

## Key facts

- **NIH application ID:** 10920408
- **Project number:** 5R01DK129541-02
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Kuang-Yu Jen
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $532,457
- **Award type:** 5
- **Project period:** 2023-09-06 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10920408

## Citation

> US National Institutes of Health, RePORTER application 10920408, Computational Image Analysis of Renal Transplant Biopsies to Predict Graft Outcome (5R01DK129541-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10920408. Licensed CC0.

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