# Development and Validation of a Deep Learning system to estimate Interstitial Fibrosis from a kidney ultrasonography image

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2023 · $358,024

## Abstract

PROJECT SUMMARY
 Interstitial fibrosis is a common finding on kidney biopsy, and strongly predicts future decline in kidney function
irrespective of the underlying etiology of kidney disease. Unfortunately, interstitial fibrosis is poorly captured by
the current clinical biomarkers of kidney function (eGFR and albuminuria). Thus, interstitial fibrosis is common,
holds substantial prognostic importance, and yet clinicians are blind to its presence or severity except in rare
instances when kidney biopsies are performed. Concurrently, new drugs are being tested to limit kidney
interstitial fibrosis, but there are no non-invasive methods to assess changes in fibrosis over time. Interstitial
fibrosis is currently estimated from histopathological examination of a kidney biopsy, which are rarely done. A
non-invasive test to estimate interstitial fibrosis is not currently available. Our exciting preliminary data
demonstrated that use of routine ultrasonography (USG) of the kidney, interpreted by deep learning/artificial
intelligence can non-invasively assess the presence and severity of interstitial fibrosis. The overarching goal of
this study is to further develop, and internally and externally validate a deep learning-based algorithm to estimate
interstitial fibrosis from USG images of the kidney relative to the kidney biopsy gold standard. We hypothesize
that, embedded within a kidney USG image are interstitial fibrosis corelates that can be extracted by deep
learning and quantitatively analyzed to estimate interstitial fibrosis with high precision, and will improve prediction
of longitudinal decline in kidney function. If so, given the widespread availability of kidney USG world-wide, this
non-invasive estimate of interstitial fibrosis would have immediate clinical implications with improved
prognostication, and ability to serially monitor interstitial fibrosis in response to therapy. The proposed program
of research will address three specific aims: Aim 1. To further develop and internally validate a deep learning-
based system for interstitial fibrosis quantification from kidney USG image. In Aim 2, we will externally validate
the performance of the deep learning model using an independent cohort of USG images and kidney biopsies,
and evaluate performance across strata of age, gender, and body size. Finally, in Aim 3, we will determine if the
USG deep learning-based interstitial fibrosis score is associated with kidney disease progression with similar
strengths relative to kidney biopsy assessment of interstitial fibrosis. Upon completion of this program of
research, we envision development of an app. / plug-in for ultrasound reading modules that would facilitate
widespread dissemination of the deep-learning tool, such that USG-based fibrosis scoring is widely available to
treating clinicians.

## Key facts

- **NIH application ID:** 10781840
- **Project number:** 1R01DK138395-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Ambarish Athavale
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $358,024
- **Award type:** 1
- **Project period:** 2023-09-22 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10781840

## Citation

> US National Institutes of Health, RePORTER application 10781840, Development and Validation of a Deep Learning system to estimate Interstitial Fibrosis from a kidney ultrasonography image (1R01DK138395-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10781840. Licensed CC0.

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