AI-enhanced MRI for detecting and classifying fatty liver disease
Official title: Quantitative MRI and Deep Learning Technologies for Classification of NAFLD
Funded at University of California Los Angeles · NIH-11325723 · Data updated 2026-11-12
This project uses faster, motion-robust MRI together with artificial intelligence to improve detection and classification of non-alcoholic fatty liver disease (NAFLD) for patients who need liver evaluation.
Quick facts
| Grant type | U01 cooperative agreement |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California Los Angeles NIH-funded |
| Lab location | 1 site (Los Angeles, United States) |
| Project start | August 1, 2022 |
| Project end | April 30, 2027 |
| Funding year | 2026 |
| Award (this year) | $608,848 |
| NIH project number | 5U01EB031894-05 |
| Project ID | NIH-11325723 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults with known or suspected NAFLD/NASH—for example those with fatty liver on ultrasound or abnormal liver tests—who can travel for MRI scans at the study site.
✕Less likely to help
People with non-NAFLD causes of liver disease, those who cannot undergo MRI (such as patients with incompatible implants or severe claustrophobia), or those with end-stage liver disease may not benefit from this project.
What this research studies
You would get quicker liver MRI scans designed to be easier to tolerate and less affected by breathing motion. The team will apply advanced image reconstruction and deep learning to remove artifacts and extract measures of liver fat, iron, and stiffness. They plan to compare those MRI measures with biopsy and clinical information to teach the AI how to recognize early signs of NASH. The aim is to make MRI more reliable and to spot disease earlier so doctors can treat it sooner.
Why it matters
Potential benefit: This could reduce the need for invasive liver biopsies and help catch and treat progressive NASH earlier.
How similar studies have gone: Quantitative MRI and elastography already help measure liver fat and stiffness noninvasively, but combining motion-robust scans with deep learning for early NASH classification is a newer approach.
Where this research is happening
-
University of California Los Angeles
Los Angeles, United States Active
Researchers
- Principal investigator: Wu, Holden H — University of California Los Angeles
- Study coordinator: Wu, Holden H
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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.