# A new robotic AI imaging platform for improved kidney disease research and drug discovery

> **NIH NIH R43** · SONOVOL, INC. · 2020 · $299,911

## Abstract

Abstract
Approximately 37M people in the USA are affected by chronic kidney disease (CKD), and it is the 9th leading
cause of death in the USA. Complications resulting from CKD are common, and the disease is an enormous
financial burden for the US health care system. Despite substantial efforts of investigators around the country,
a pharmaceutical treatment to directly reverse the process of fibrosis (a key physiological process in CKD) and
regenerate nephrons has remained elusive. The demand for novel therapeutics is only expected to grow.
Animal models are the gold standard for basic and applied kidney disease research, but there are no widely
used noninvasive imaging tools for assessing renal fibrosis within these models; this represents a major
limitation in the kidney disease research and drug development fields. The work proposed herein will result in a
platform that will dramatically accelerate the pace of basic and applied research in CKD.
SonoVol is proposing to build and validate a novel renal fibrosis imaging platform and validate it against both in
vivo and ex vivo gold standards. The prototype system will be deployed within SonoVol’s VegaTM platform, a
robotically controlled volumetric scanning device for high-throughput rodent imaging. This will remove existing
bottlenecks and ensure accurate and repeatable renal fibrosis measures. This work will proceed via three
stages: First, we will develop hardware to allow accurate and repeatable sampling of ultrasound elastography
images by adding a rotational degree of freedom, enabling long and short axis measurements regardless of
how a kidney is positioned. Second, we will develop software to facilitate automated kidney localization and
acquisition of multiple ultrasound stiffness mapping modes to allow fibrosis to be evaluated whether or not a
kidney contains fluid filled cysts. Finally, we will validate the platform using in vitro phantoms and then two
different in vivo models for renal fibrosis. The data will be compared to gold standards for both in vivo and ex
vivo readouts.

## Key facts

- **NIH application ID:** 10154589
- **Project number:** 1R43DK126607-01A1
- **Recipient organization:** SONOVOL, INC.
- **Principal Investigator:** Tomasz Joseph Czernuszewicz
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $299,911
- **Award type:** 1
- **Project period:** 2020-09-15 → 2021-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10154589

## Citation

> US National Institutes of Health, RePORTER application 10154589, A new robotic AI imaging platform for improved kidney disease research and drug discovery (1R43DK126607-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10154589. Licensed CC0.

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