Using AI to analyze kidney ultrasound images for fibrosis assessment
Official title: Development and Validation of a Deep Learning system to estimate Interstitial Fibrosis from a kidney ultrasonography image
Funded at University of California, San Diego · NIH-10932906 · Data updated 2026-11-12
This study is testing a new AI tool that looks at kidney ultrasound images to help doctors find and understand kidney damage without needing to do a painful biopsy, making it easier for patients to keep track of their kidney health.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California, San Diego NIH-funded |
| Lab location | 1 site (La Jolla, United States) |
| Project start | September 22, 2023 |
| Project end | July 31, 2028 |
| Funding year | 2024 |
| Award (this year) | $313,396 |
| NIH project number | 5R01DK138395-02 |
| Project ID | NIH-10932906 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals with chronic kidney disease or those at risk of kidney fibrosis.
✕Less likely to help
Patients with acute kidney conditions or those who do not undergo kidney ultrasound imaging may not benefit from this research.
What this research studies
This research aims to develop a deep learning system that can analyze kidney ultrasound images to estimate the presence and severity of interstitial fibrosis, a condition that can lead to kidney function decline. Currently, interstitial fibrosis is often undetected due to reliance on invasive kidney biopsies and traditional biomarkers. By utilizing artificial intelligence, this project seeks to create a non-invasive method that can provide valuable insights into kidney health over time. The study will validate this AI system against the gold standard of kidney biopsy results.
Why it matters
Potential benefit: If successful, this research could provide a non-invasive tool for early detection and monitoring of kidney fibrosis, potentially improving patient outcomes.
How similar studies have gone: Other research has shown promise in using AI for medical imaging analysis, indicating potential success for this novel approach.
Where this research is happening
-
University of California, San Diego
La Jolla, United States Active
Researchers
- Principal investigator: Athavale, Ambarish — University of California, San Diego
- Study coordinator: Athavale, Ambarish
Related research
Other NIH-funded projects on the same topics.
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.