The Glaucoma and Retinopathy Screening Study
Purpose
The goal of this clinical trial is to learn if a new screening approach including an artificial intelligence algorithm that analyzes fundus photographs, measurement of eye pressure and visual field testing works to screen for glaucoma. Participants will: Have an image of their fundus (back of the eye) taken as part of their diabetic eye screening Have a measurement of their eye pressure If needed, have a test of their side vision using a headset
Condition
- Glaucoma
Eligibility
- Eligible Ages
- Over 40 Years
- Eligible Sex
- All
- Accepts Healthy Volunteers
- No
Criteria
- Individuals with diabetes undergoing AI-based screening for diabetic retinopathy
using the LumineticsCore (Digital Diagnostics) system for clinical care at primary
care centers.
- Individuals who are able and willing to provide informed consent for participation
in the study.
Study Design
- Phase
- N/A
- Study Type
- Interventional
- Allocation
- Non-Randomized
- Intervention Model
- Parallel Assignment
- Intervention Model Description
- This study uses a prospective interventional model to evaluate the effectiveness of integrating AI-based glaucoma screening with existing diabetic eye disease (DED) screening among diabetic patients. Participants will have fundus images assessed by AI for glaucoma in addition to DED, and have intraocular pressure measurement measured. Suspected glaucoma cases will receive virtual perimetry testing for confirmation, and those diagnosed with glaucoma will be referred for follow-up care. This study aims to compare glaucoma detection rates between combined DED and glaucoma screening versus DED-only screening, ultimately supporting early glaucoma detection and enhancing care access in underserved communities.
- Primary Purpose
- Screening
- Masking
- None (Open Label)
Arm Groups
| Arm | Description | Assigned Intervention |
|---|---|---|
|
Experimental Combined DED and Glaucoma Screening |
Participants in this arm will have fundus photographs that are taken as a part of standard clinical care analyzed by AI for signs of glaucoma in addition to for diabetic retinopathy. They will also have intraocular pressure measured. If the AI detects possible glaucoma, participants will undergo virtual perimetry testing for further assessment. |
|
|
No Intervention DED Screening Only (Control Arm) |
Participants in this arm will undergo diabetic eye disease (DED) screening only for routine clinical care. This includes fundus photography for AI assessment of signs of diabetic retinopathy. |
|
Recruiting Locations
Chelsea, Massachusetts 02150
More Details
- Status
- Recruiting
- Sponsor
- Massachusetts Eye and Ear Infirmary
Detailed Description
Study Overview: This study is a prospective, interventional clinical trial designed to evaluate the effectiveness of an artificial intelligence (AI)-based screening program within community health settings. This study targets especially diabetic patients because they have higher risks of developing glaucoma. By integrating glaucoma screening into existing diabetic eye disease (DED) screenings, the study aims to identify cases of glaucoma earlier, thereby preventing or delaying progression to blindness. Background: Glaucoma is a chronic eye disease that causes progressive optic nerve damage, often leading to irreversible vision loss. Early detection is critical, as glaucoma is typically asymptomatic in its early stages. Individuals with diabetes are at an elevated risk for glaucoma, making it crucial to develop accessible screening methods. Current DED screening programs already utilize fundus photography for diabetic retinopathy. Adding glaucoma screening to these existing DED screenings may provide an efficient and cost-effective solution to reach high-risk populations without requiring additional clinic visits. Study Hypothesis: The hypothesis of this study is that incorporating AI-driven glaucoma screening into standard DED screenings will increase the detection rate of glaucoma in high-risk populations compared to DED screening alone. This combined approach is expected to yield better clinical outcomes by enabling early diagnosis and treatment while being cost-effective. Expected Outcomes and Impact: This study is expected to provide valuable insights into the effectiveness of integrating AI-based glaucoma screening into existing screening programs for diabetic eye disease. If successful, this combined screening approach could be a cost-effective model for other community health settings, leading to earlier detection of glaucoma and improved patient outcomes. By making glaucoma screening more accessible the study aims to reduce health disparities and support preventive eye care.