AI to detect and track glaucoma earlier
SCH: Advancing Clinical Decision Support for Glaucoma Detection and Progression Using Multi-Modal AI
This project builds an AI tool to help eye doctors spot glaucoma sooner and predict vision changes for patients who get retinal scans and visual field tests.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Pennsylvania NIH-funded |
| Lab location | 1 site (Philadelphia, United States) |
| Project ID | NIH-11160763 on NIH RePORTER |
What this research studies
If you join, researchers will combine different kinds of eye data—like retinal images and visual field tests taken over time—to train an AI that recognizes signs of glaucoma and predicts vision loss. The team will prioritize making the AI's results understandable to doctors by showing how confident predictions are and explaining key findings to support safe decisions. They will test the tool across diverse patient data and clinic settings to help ensure it works well for people from different backgrounds and with different types of eye exams. The goal is a decision-support tool that fits into clinic workflows and helps doctors and patients act earlier to protect vision.
Who could benefit from this research
Good fit: Ideal candidates are people who have or are at risk for glaucoma and who have retinal imaging and visual field test records or are willing to have these exams.
Not a fit: People without standard retinal imaging or visual field testing, or whose vision loss is due to non-glaucoma conditions, may not benefit from this tool.
Why it matters
Potential benefit: It could help catch glaucoma earlier, guide treatment choices, and reduce preventable vision loss.
How similar studies have performed: Previous AI tools using single imaging types have shown promise for glaucoma detection, but combining multiple data types with clear explanations and uncertainty estimates is less well tested.
Where this research is happening
Philadelphia, United States
- University of Pennsylvania — Philadelphia, United States (Active)
Researchers
- Principal investigator: Lee, Insup — University of Pennsylvania
- Study coordinator: Lee, Insup
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.