# A new approach to predict graft outcome from histology in children undergoing kidney transplantation using machine learning methods

> **NIH NIH R21** · EMORY UNIVERSITY · 2020 · $191,618

## Abstract

Abstract
Although the overall renal graft survival has dramatically improved over the last several decades,
almost half of pediatric patients lose their transplant within 10 years. Epidemiology, immunology
and histology are all key elements in understanding post-transplant complications and in
predicting clinical outcomes, however accurate prediction of transplant outcomes remains
challenging and understudied among pediatric kidney transplant recipients. Machine learning
technology is rapidly advancing and has demonstrated remarkable predictive accuracy
surpassing classical predictive models in several biomedical applications. The goal of this
research proposal is to develop machine learning methods to improve prediction of transplant
outcomes through analysis of histology, assemble data sufficient to describe the incidence of
different types of rejection and histological findings on pediatric kidney transplant biopsies, and to
assess their impact on graft outcome.
To achieve these goals, we will assemble an international multicenter cohort of over 700 pediatric
kidney transplant recipients and develop and validate machine-learning models combining
clinical, immunological and histological data for predicting short-term pediatric graft outcomes.
This study will enable accurate, reproducible, and standardized assessment of histopathological
findings in pediatric transplant recipients and provide a new method to predict graft outcome. This
will pave the way to future large, prospective study assessing the feasibility and potential impact
of machine-learning methods in transplant biopsy analysis, with the goal of improving clinical
management and resource utilization by identifying patients with high risk of graft loss. Finally,
this study will provide critical information for efficient and effective design of clinical trials focused
on improving outcomes of pediatric kidney transplant recipients, with an emphasis on
individualized treatment.

## Key facts

- **NIH application ID:** 10007806
- **Project number:** 5R21DK122229-02
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** Julien Hogan
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $191,618
- **Award type:** 5
- **Project period:** 2019-09-04 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10007806

## Citation

> US National Institutes of Health, RePORTER application 10007806, A new approach to predict graft outcome from histology in children undergoing kidney transplantation using machine learning methods (5R21DK122229-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10007806. Licensed CC0.

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