Using routine lab tests and health records to spot diabetic eye disease earlier
Official title: SCH: Harnessing Tensor Information to Improve EHR Data Quality for Accurate Data-driven Screening of Diabetic Retinopathy with Routine Lab Results
Funded at Oklahoma State University Stillwater · NIH-11233050 · Data updated 2026-11-12
This project uses patterns in routine blood tests and electronic health records to help identify adults with diabetes who may have early diabetic retinopathy so they can get timely eye care.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Oklahoma State University Stillwater NIH-funded |
| Lab location | 1 site (Stillwater, United States) |
| Project start | September 30, 2021 |
| Project end | August 31, 2026 |
| Funding year | 2025 |
| Award (this year) | $67,169 |
| NIH project number | 3R01EY033861-04S1 |
| Project ID | NIH-11233050 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults with diabetes (age 21 and older) who receive routine primary-care lab testing and whose records are in participating electronic health record systems are the most likely candidates.
✕Less likely to help
People without available electronic health record data, children, or those already receiving regular retinal imaging from an eye specialist may not gain additional benefit from this project.
What this research studies
You would not need a retinal camera for this approach; researchers are training computer models to read routine lab results and other clinical data already in your medical record to flag people at higher risk for diabetic retinopathy. The team is improving data quality in electronic health records so the models work reliably across different clinics. The goal is to let primary care teams identify at-risk patients during regular visits and recommend eye exams sooner. This could make screening more affordable and easier to deliver in rural or resource-limited settings.
Why it matters
Potential benefit: If successful, this could help more people with diabetes get earlier eye exams and reduce preventable vision loss by making screening easier and more widely available.
How similar studies have gone: Some prior machine-learning work using EHR data to flag disease risk has shown promise, but using routine labs instead of retinal images for diabetic retinopathy screening is still relatively new.
Where this research is happening
-
Oklahoma State University Stillwater
Stillwater, United States Active
Researchers
- Principal investigator: Liu, Tieming — Oklahoma State University Stillwater
- Study coordinator: Liu, Tieming
Related research
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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.