Using deep learning to detect and predict glaucoma progression from eye scans
Official title: Deep Learning Approaches to Detect Glaucoma and Predict Progression from Spectral Domain Optical Coherence Tomography
Funded at University of California, San Diego · NIH-11081725 · Data updated 2026-11-12
This study is looking at how we can better spot and track primary open angle glaucoma using advanced computer technology, so that patients can get the help they need sooner and manage their condition more effectively.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California, San Diego NIH-funded |
| Lab location | 1 site (La Jolla, United States) |
| Project start | April 1, 2023 |
| Project end | March 31, 2026 |
| Funding year | 2025 |
| Award (this year) | $249,000 |
| NIH project number | 5R00EY030942-05 |
| Project ID | NIH-11081725 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals diagnosed with primary open angle glaucoma or those at risk of developing the condition.
✕Less likely to help
Patients with other types of glaucoma or those who do not have access to the necessary imaging technologies may not benefit from this research.
What this research studies
This research focuses on improving the detection and monitoring of primary open angle glaucoma (POAG), a leading cause of blindness. By utilizing advanced deep learning techniques, the study aims to analyze data from optical coherence tomography (OCT) and visual field tests, along with patient demographics and medical history. The goal is to create predictive models that can accurately forecast the progression of glaucoma, allowing for earlier intervention and better management of the disease.
Why it matters
Potential benefit: If successful, this research could lead to earlier detection and more effective treatment strategies for patients with glaucoma, potentially preserving vision.
How similar studies have gone: Other research has shown promising results using deep learning approaches for similar diagnostic challenges, indicating potential for success in this area.
Where this research is happening
-
University of California, San Diego
La Jolla, United States Active
Researchers
- Principal investigator: Christopher, Mark — University of California, San Diego
- Study coordinator: Christopher, Mark
Related research
Other NIH-funded projects on the same topics.
-
Population-wide deep learning glaucoma screening Models
Aims to create AI eye-screening tools that work well for diverse populations so people can get easy glaucoma checks from primary care or pharmacies.
-
AI screening that detects glaucoma across diverse populations
This project builds AI that reads retinal photos and OCT scans to find glaucoma early for people from different racial and ethnic backgrounds.
-
Finding Glaucoma Vision Loss Early with Computer Learning
This project uses advanced computer learning to help doctors find vision changes in glaucoma patients sooner, especially for veterans.
-
Creating fair AI models for glaucoma screening
This study is working on using smart computer technology to help find glaucoma early, especially for people from different backgrounds who might not have easy access to eye care, so that everyone can get the right diagnosis without needing expensive specialist visits.
-
IOP and CSF Pressure in Glaucoma Risk
This grant studies how eye pressure and cerebrospinal fluid pressure together influence glaucoma risk and vision loss.
-
Integrative histo-genomic analysis to guide cancer risk and targeted therapies with interpretable AI
Researchers are developing an advanced AI-powered approach to map genetic and cellular patterns across breast and cervical cancers to help tailor treatments.
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.