Using advanced AI to predict vision loss in patients with age-related macular degeneration
Domain-adaptive federated learning to develop machine learning models for predicting incident and progression of geographic atrophy
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 ID | NIH-10889689 on NIH RePORTER |
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.
Who could benefit from this research
Good fit: 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.
Not a fit: Patients with advanced geographic atrophy or those without a diagnosis of age-related macular degeneration may not benefit from this research.
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 performed: 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
Charlotte, United States
- 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
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 below.