Using machine learning to improve diabetic retinopathy screening for Veterans
Official title: Prediction Tools for Diabetic Retinopathy Among Veterans Using Machine Learning and Large-scale Data Analysis Techniques
Funded at VA Eastern Colorado Health Care System · NIH-11052679 · Data updated 2026-11-12
This study is working to improve eye screenings for Veterans with diabetes by using smart technology to find those at the highest risk of vision loss, so they can get the right care when they need it most.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | VA Eastern Colorado Health Care System NIH-funded |
| Lab location | 1 site (Aurora, UNITED STATES) |
| Project start | April 1, 2025 |
| Project end | March 31, 2030 |
| Funding year | 2025 |
| NIH project number | 1IK2CX002805-01A1 |
| Project ID | NIH-11052679 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are Veterans diagnosed with diabetes who are at risk for developing diabetic retinopathy.
✕Less likely to help
Patients who do not have diabetes or are not Veterans may not benefit from this research.
What this research studies
This research aims to enhance diabetic retinopathy screening for Veterans by developing advanced machine learning tools that analyze large-scale data from the VA healthcare system. The project focuses on identifying Veterans at high risk for vision loss due to diabetic retinopathy, a condition that affects them more severely than the general population. By personalizing screening strategies based on individual risk factors, the research seeks to ensure that high-risk patients receive timely and appropriate care. The approach leverages the extensive electronic medical records available within the VA system to improve patient outcomes.
Why it matters
Potential benefit: If successful, this research could lead to more accurate and timely screenings for diabetic retinopathy, potentially preventing vision loss in Veterans.
How similar studies have gone: Previous research has shown that machine learning approaches can effectively improve screening and management of diabetic conditions, indicating potential success for this novel application.
Where this research is happening
-
VA Eastern Colorado Health Care System
Aurora, United States Active
Researchers
- Principal investigator: Christopher, Karen — VA Eastern Colorado Health Care System
- Study coordinator: Christopher, Karen
Related research
Other NIH-funded projects on the same topics.
-
Integrative multi-omics to understand diabetic retinopathy
A doctor-scientist team will study blood markers and genetic signals to see why diabetic retinopathy happens and how it affects Hispanic/Latino people.
-
Smartphone retinal imaging for diabetic eye disease in Egypt
This project uses a low-cost smartphone camera and AI to find signs of diabetic eye damage in adults with diabetes in Egypt.
-
Retinal Cell Loss in Diabetic Retinopathy
This project looks at how mitochondrial problems and a specific kind of cell death called ferroptosis may cause retinal cells to die in people with diabetes-related eye disease.
-
Redox-triggered REDD1 blockers for diabetic retinopathy
A research project tests new ways to stop a stress protein (REDD1) from causing vision problems in people with diabetic retinopathy.
-
Ultrafast eye imaging to detect and track diabetic retinopathy
A doctor-science team is developing ultra-fast eye scans to better see tiny blood vessels in people with diabetic retinopathy, helping catch changes early and guide treatment.
-
Practical device to automatically track abnormal retinal blood vessel growth in diabetic eye disease
A new, easier-to-use imaging device aims to automatically detect and monitor abnormal retinal blood vessel growth in people with diabetic retinopathy.
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.