# A Deep Learning Model to Improve Pathologist Interpretation of Donor Kidney Biopsies

> **NIH NIH R42** · NEWVENTUREIQ, LLC · 2020 · $811,695

## Abstract

ABSTRACT
More people die every year from kidney disease than breast or prostate cancer. Kidney
transplantation is life-saving, yet the donor organ shortage and high organ discard rate
contributes to 13 deaths daily among patients awaiting transplant. The decision to use or
discard a donor kidney relies heavily on microscopic quantitation of chronic damage by
pathologists. The current standard of care relies on a manual process that is subject to
significant human variability and inefficiency, resulting in potentially healthy kidneys being
discarded and potentially damaged kidneys being transplanted inappropriately. Our team
developed the first Deep Learning model to quantify percent global glomerulosclerosis in donor
kidney frozen section biopsy whole slide images. We developed a cloud-based platform to apply
the Deep Learning model to analyze kidney biopsy whole slide images in under 6 minutes with
accuracy and precision equal to or greater than current standard of care pathologists. We have
also developed a Deep Learning model to quantify interstitial fibrosis on donor kidney biopsy
whole slide images. This innovative approach has the potential to transform donor kidney biopsy
evaluation by improving pathologist efficiency, accuracy, and precision ultimately resulting in
optimized donor organ utilization, improved patient outcomes, and diminished health care costs.
The goal of this project is to establish our Deep Learning automated techniques as the standard
for evaluating donor kidneys prior to transplantation. This will be achieved by assembling a team
of expert pathologists and computer scientists specializing in machine learning. The proposal
will evaluate the accuracy and precision of the interstitial fibrosis Deep Learning model, use the
automated quantitation of key microscopic findings to develop an outcome-based chronic
damage score that predicts graft outcome, and test the ability of the Deep Learning models to
withstand variations encountered using different scanners and processing in different
laboratories. The functionality of the Trusted Kidney software platform will be improved beyond
the current usable product into a commercially viable solution for multiple laboratories.

## Key facts

- **NIH application ID:** 10138826
- **Project number:** 2R42DK120253-02
- **Recipient organization:** NEWVENTUREIQ, LLC
- **Principal Investigator:** Joseph P Gaut
- **Activity code:** R42 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $811,695
- **Award type:** 2
- **Project period:** 2018-09-21 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10138826

## Citation

> US National Institutes of Health, RePORTER application 10138826, A Deep Learning Model to Improve Pathologist Interpretation of Donor Kidney Biopsies (2R42DK120253-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10138826. Licensed CC0.

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