Improving glaucoma risk prediction with genetics and AI
Enhancing Glaucoma Risk Prediction through Advanced Genomics and Machine Learning
['FUNDING_R01'] · MASSACHUSETTS EYE AND EAR INFIRMARY · NIH-11160777
This project combines genetic information and artificial intelligence to create more accurate glaucoma risk scores for people at risk of or living with glaucoma.
Quick facts
| Phase | ['FUNDING_R01'] |
|---|---|
| Study type | Nih_funding |
| Sex | All |
| Sponsor | MASSACHUSETTS EYE AND EAR INFIRMARY (nih funded) |
| Locations | 1 site (BOSTON, UNITED STATES) |
| Trial ID | NIH-11160777 on ClinicalTrials.gov |
What this research studies
You may hear that researchers are using the text in medical records, genetic data, and eye images to build better tools that predict who will develop or worsen from glaucoma. They will use AI to read doctors' notes and refine how glaucoma is identified from records, run large genetic analyses across more than a million participants, and combine those results with machine-learning features from eye structure to make stronger risk scores. The work is led at a major eye center and uses data from multiple hospitals and biobanks so findings could apply to many people. If successful, these tools could help doctors find high-risk patients earlier and tailor monitoring or treatment.
Who could benefit from this research
Good fit: Ideal candidates are adults with glaucoma, people with a family history of glaucoma, or patients receiving eye care at participating centers who can share medical records, genetic data, or eye imaging.
Not a fit: People without available genetic data, eye imaging, or linkage to the participating medical centers/biobanks, and those with eye conditions unrelated to glaucoma, are unlikely to see direct benefit from this project.
Why it matters
Potential benefit: If successful, this could identify people at high risk for glaucoma earlier and help target monitoring or treatment to prevent vision loss.
How similar studies have performed: Prior studies show polygenic risk scores can predict glaucoma risk to a moderate degree, but combining refined electronic-health-record phenotypes and imaging-derived machine-learning features is newer and may substantially improve performance.
Where this research is happening
BOSTON, UNITED STATES
- MASSACHUSETTS EYE AND EAR INFIRMARY — BOSTON, UNITED STATES (ACTIVE)
Researchers
- Principal investigator: ZEBARDAST, NAZLEE — MASSACHUSETTS EYE AND EAR INFIRMARY
- Study coordinator: ZEBARDAST, NAZLEE
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.