Using advanced AI to predict vision loss in patients with age-related macular degeneration
Official title: Domain-adaptive federated learning to develop machine learning models for predicting incident and progression of geographic atrophy
Funded at University of North Carolina Charlotte · NIH-10889689 · Data updated 2026-11-12
This study is working on using smart computer models to help predict when geographic atrophy, a serious eye condition related to aging, might start or get worse, so that patients at higher risk can get the care they need more often while keeping their personal information safe.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of North Carolina Charlotte NIH-funded |
| Lab location | 1 site (Charlotte, United States) |
| Project start | July 1, 2024 |
| Project end | June 30, 2026 |
| Funding year | 2024 |
| Award (this year) | $379,363 |
| NIH project number | 1R21EY035271-01A1 |
| Project ID | NIH-10889689 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals diagnosed with early stages of age-related macular degeneration who are at risk of progressing to geographic atrophy.
✕Less likely to help
Patients with advanced geographic atrophy or those without a diagnosis of age-related macular degeneration may not benefit from this research.
What this research studies
This research focuses on developing machine learning models to predict the onset and progression of geographic atrophy, a severe form of age-related macular degeneration (AMD). By utilizing federated learning techniques, the study aims to analyze data from multiple institutions without compromising patient privacy. This approach allows for the creation of more accurate predictive models by leveraging diverse datasets while keeping sensitive information secure. Patients at higher risk for vision loss will be identified for more frequent follow-ups and timely interventions.
Why it matters
Potential benefit: If successful, this research could lead to earlier detection and better management of vision loss in patients with age-related macular degeneration.
How similar studies have gone: Other research has shown promise in using machine learning techniques for similar predictive tasks, indicating that this approach could be effective.
Where this research is happening
-
University of North Carolina Charlotte
Charlotte, United States Active
Researchers
- Principal investigator: Alam, Minhaj Nur — University of North Carolina Charlotte
- Study coordinator: Alam, Minhaj Nur
Related research
Other NIH-funded projects on the same topics.
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Role of immune cells in geographic atrophy and age-related macular degeneration
A physician-scientist is training to understand how microglia interact with retinal cells to influence geographic atrophy in age-related macular degeneration.
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See if STL303 helps adults with geographic atrophy from AMD
This study tests STL303 versus placebo in people 60 and older who have geographic atrophy due to age-related macular degeneration to see if it is safe and can help their vision.
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C.001 for retinal degeneration: a first-in-human dose-escalation safety test
A first-in-human study will test the safety and tolerability of a single subretinal injection of C.001 in adults with retinal diseases involving retinal pigment epithelium degeneration.
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Central microperimetry and radial OCT in geographic atrophy
We will test whether central microperimetry and radial OCT scans can more accurately measure how geographic atrophy progresses toward the fovea in people with or at risk for GA from age-related macular degeneration.
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One-time intravitreal SAR446597 injection for geographic atrophy due to AMD
This trial will try a single eye injection of SAR446597 to slow geographic atrophy in people 60 and older with AMD.
-
Dapagliflozin (an SGLT2 inhibitor) for slowing geographic atrophy in dry AMD
This trial will test whether taking dapagliflozin once daily can slow the growth of geographic atrophy in people aged 50 and older with dry age-related macular degeneration.
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.