Creating fair AI models for glaucoma screening
Official title: Developing Equitable Deep Learning Models for Automated Glaucoma Screening
Funded at Schepens Eye Research Institute · NIH-10872671 · Data updated 2026-11-12
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.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Schepens Eye Research Institute NIH-funded |
| Lab location | 1 site (Boston, United States) |
| Project start | September 30, 2024 |
| Project end | July 31, 2029 |
| Funding year | 2024 |
| Award (this year) | $754,779 |
| NIH project number | 1R01EY036222-01 |
| Project ID | NIH-10872671 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals from racial and ethnic minority groups who may be at higher risk for glaucoma and have limited access to eye care.
✕Less likely to help
Patients who do not have glaucoma or those who have already received adequate screening and treatment may not benefit from this research.
What this research studies
This research focuses on developing advanced deep learning models to detect glaucoma, a leading cause of blindness, particularly among underserved racial and ethnic groups. By utilizing retinal imaging, the project aims to provide affordable glaucoma screening that can be implemented in primary care settings, reducing the need for costly visits to specialized eye clinics. The study will assess the equity of these models by examining their performance across various demographic factors, ensuring that all groups receive accurate diagnoses. The approach includes using optical coherence tomography scans and fundus photos to predict glaucoma based on established clinical guidelines.
Why it matters
Potential benefit: If successful, this research could lead to more equitable and accessible glaucoma screening, ultimately reducing blindness rates in vulnerable populations.
How similar studies have gone: Previous research has shown promise in using AI for medical diagnostics, but this specific approach to equitable glaucoma detection is novel and has not been extensively tested.
Where this research is happening
-
Schepens Eye Research Institute
Boston, United States Active
Researchers
- Principal investigator: Wang, Mengyu — Schepens Eye Research Institute
- Study coordinator: Wang, Mengyu
Related research
Other NIH-funded projects on the same topics.
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Population-wide deep learning glaucoma screening Models
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-
AI screening that detects glaucoma across diverse populations
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-
Finding Glaucoma Vision Loss Early with Computer Learning
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-
Using deep learning to detect and predict glaucoma progression from eye scans
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.
-
IOP and CSF Pressure in Glaucoma Risk
This grant studies how eye pressure and cerebrospinal fluid pressure together influence glaucoma risk and vision loss.
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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.