Multimodal AI to predict glaucoma progression and guide care
Official title: Developing Robust Multimodal AI for Primary Open-Angle Glaucoma Prognosis
Funded at University of Minnesota · NIH-11365407 · Data updated 2026-11-12
A big-data AI tool uses eye images, scans, field tests, and health info to help predict who with open-angle glaucoma is at higher risk of vision loss so doctors can act sooner.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Minnesota NIH-funded |
| Lab location | 1 site (Minneapolis, United States) |
| Project start | July 1, 2026 |
| Project end | December 31, 2030 |
| Funding year | 2026 |
| Award (this year) | $764,310 |
| NIH project number | 1R01EY037701-01A1 |
| Project ID | NIH-11365407 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults diagnosed with primary open-angle glaucoma or at elevated risk, who undergo multimodal eye imaging and clinical assessments.
✕Less likely to help
Patients without POAG or without access to multimodal imaging data may not benefit directly.
What this research studies
This project develops robust deep learning models that combine multiple eye data types and patient health information to forecast POAG prognosis. It focuses on handling missing data and ensuring the model works well across diverse patient groups, with input from glaucoma specialists to keep it clinically usable. The work includes unsupervised feature learning to integrate fundus photos, OCT, visual fields, and clinical variables, aiming to provide timely risk scores that support decision-making in real-world clinics. The team plans iterative testing and refinement in collaboration with clinicians to fit existing workflows. By the end, clinicians may have a reliable, automated tool to identify high-risk patients and tailor monitoring or treatment decisions.
Why it matters
Potential benefit: If successful, patients at higher risk could receive earlier interventions to prevent vision loss and tailor follow-up care.
How similar studies have gone: Prior studies show promise for multimodal AI in ophthalmology, but robust, clinically integrated prognosis tools remain an emerging area.
Where this research is happening
-
University of Minnesota
Minneapolis, United States Active
Researchers
- Principal investigator: Lin, Mingquan — University of Minnesota
- Study coordinator: Lin, Mingquan
Related research
Other NIH-funded projects on the same topics.
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This project will build more accurate genetic risk tools for people at risk of open-angle glaucoma by including more diverse ancestry groups in the analysis.
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This study is working on a new AI tool to help better predict the risk of glaucoma, especially for people in diverse communities like African and Hispanic Americans, so that everyone can get fairer and more accurate eye care.
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.