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Accurate evaluations make every donated kidney count.

A small change can make a big increase in the number of successful transplants.

The Problem

100,000

Patients waiting for a donor kidney (5,000 die every year waiting)

3,500

1

Kidneys procured for transplantation are discarded every year

3x

Kidneys that are biopsied are three times more likely to be discarded

Adoption of Trusted Kidney in biopsy evaluation has the potential to decrease an institution’s organ discard rate.

37% 
1
decrease in erroneous organ discard

Artificial Intelligence can help solve the donor organ shortage. At Trusted Kidney, we are doing our part to reduce organ discard. AI-powered biopsies give more accurate results, leading to more transplants and better patient outcomes.

Kidney Slide Example

Deep Learning Model for Global Glomerulosclerosis

Researchers developed a Deep Learning model that identifies and classifies non-sclerosed and sclerosed glomeruli in whole-slide images of donor kidney frozen section biopsies. The model performed as well as expert pathologists, and can be used to support more accurate biopsy evaluation.

A Pathologist with a microscope
22%
potential decrease
2
in pathologist error rates
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Developed for pathologists

by pathologists

Trusted Kidney can help labs provide more accurate biopsy results.

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For Organ Procurement Organizations

Trusted Kidney can partner with you to optimize donor organ placement.

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© 2025 by Trusted Kidney

Info@trustedkidney.com • BioGenerator Labs, Suite 300 - 4340 Duncan Avenue • St Louis, MO 63110

Trusted Kidney is for Research Use Only and is not for use in diagnostic procedures.

1 Mohan S., et al. “Factors leading to the discard of deceased donor kidneys in the United States.” Kidney Int. 2018:94:187-198.

​2  Azancot MA, Moreso F, Salcedo M, Cantarell C, Perello M, Torres IB, et al.: The reproducibility and predictive value on outcome of renal biopsies from expanded criteria donors. Kidney Int, 85: 1161-1168, 2014 10.1038/ki.2013.461
3  Marsh JN, Liu TC, Wilson PC, Swamidass SJ, Gaut JP. “Development and validation of a deep learning model to quantify glomerulosclerosis in kidney biopsy specimens.” JAMA Netw Open. 2021:4(1):e2030939.
 

SOURCES:

Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections”, Marsh JN, Matlock MK, Kudose S, Liu TC, Stappenbeck TS, Gaut JP, Swamidass SJ., IEEE Trans Med Imaging. 2018 Dec;37(12):2718-2728. doi: 10.1109/TMI.2018.2851150

Gaut J, Marsh J, Blackford M, Swamidass V, Berry R, Swamidass S. Deployment of a Deep Learning Model to Assist Pathologists with Donor Kidney Biopsy Evaluation. 2021. Poster presented at the Digital Pathology Association annual conference.

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