Using machine learning to improve diagnosis and prediction of diabetic retinopathy
Official title: Distributed approaches to train machine learning models in diabetic retinopathy
Funded at University of North Carolina Charlotte · NIH-10795486 · Data updated 2026-11-12
This study is working on smart computer programs that help doctors better spot and predict diabetic retinopathy using special eye scans, all while keeping your personal information safe, so that patients can get earlier and more effective treatment for their eye health.
Quick facts
| Grant type | R15 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of North Carolina Charlotte NIH-funded |
| Lab location | 1 site (Charlotte, United States) |
| Project start | March 1, 2024 |
| Project end | February 28, 2027 |
| Funding year | 2024 |
| Award (this year) | $459,516 |
| NIH project number | 1R15EY035804-01 |
| Project ID | NIH-10795486 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals diagnosed with diabetic retinopathy or those at risk of developing this condition.
✕Less likely to help
Patients without diabetic retinopathy or those with unrelated eye conditions may not benefit from this research.
What this research studies
This research focuses on developing advanced machine learning models to enhance the diagnosis and prediction of diabetic retinopathy (DR) using optical coherence tomography angiography (OCTA). By employing a distributed approach, the project aims to train these models across multiple institutions without sharing sensitive patient data, thus addressing privacy concerns. The goal is to create robust algorithms that can accurately classify different stages of DR and predict the progression of proliferative diabetic retinopathy. This innovative method seeks to improve early detection and treatment outcomes for patients suffering from this condition.
Why it matters
Potential benefit: If successful, this research could lead to more accurate and timely diagnoses of diabetic retinopathy, potentially preventing severe vision loss in patients.
How similar studies have gone: Previous research has shown promise in using machine learning for medical diagnostics, 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
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Ultrafast eye imaging to detect and track diabetic retinopathy
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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.