The Glaucoma and Retinopathy Screening Study (GRaSS)
The Glaucoma and Retinopathy Screening Study (GRaSS)
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
Study Overview
Status
Conditions
Intervention / Treatment
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.
Study Type
Enrollment (Estimated)
Phase
- Not Applicable
Contacts and Locations
Study Contact
- Name: David S Friedman, MD, PhD, MPH
- Phone Number: 617-573-3094
- Email: grass@mgb.org
Study Locations
-
-
Massachusetts
-
Boston, Massachusetts, United States, 02115
- Recruiting
- Brigham and Women's Primary Care Associates of Longwood
-
Contact:
- David S Friedman, MD, PhD, MPH
- Phone Number: 617-573-3094
- Email: grass@mgb.org
-
Boston, Massachusetts, United States, 02114
- Recruiting
- Massachusetts General Hospital Internal Medicine Associates
-
Contact:
- David S Friedman, MD, PhD, MPH
- Phone Number: 617-573-3094
- Email: grass@mgb.org
-
Boston, Massachusetts, United States, 02151
- Recruiting
- Massachusetts General Hospital Broadway Primary Care-Revere
-
Contact:
- David S Friedman, MD, PhD, MPH
- Phone Number: 617-573-3094
- Email: grass@mgb.org
-
Chelsea, Massachusetts, United States, 02150
- Recruiting
- MGH Chelsea HealthCare Center
-
Contact:
- David S Friedman, MD, PhD, MPH
- Phone Number: 617-573-3094
- Email: grass@mgb.org
-
Lynn, Massachusetts, United States, 01904
- Recruiting
- NSPG Lynn
-
Contact:
- David S Friedman, MD, PhD, MPH
- Phone Number: 617-573-3094
- Email: grass@mgb.org
-
Salem, Massachusetts, United States, 01970
- Not yet recruiting
- NSPG Salem
-
Contact:
- David S Friedman, MD, PhD, MPH
- Phone Number: 617-573-3094
- Email: grass@mgb.org
-
Saugus, Massachusetts, United States, 01906
- Recruiting
- NSPG Saugus
-
Contact:
- David S Friedman, MD, PhD, MPH
- Phone Number: 617-573-3094
- Email: grass@mgb.org
-
-
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Description
- Individuals with diabetes undergoing AI-based screening for diabetic retinopathy using the LumineticsCore (Digital Diagnostics) system at primary care centers.
- Individuals who are able and willing to provide informed consent for participation in the study.
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Screening
- Allocation: Non-Randomized
- Interventional Model: Parallel Assignment
- Masking: None (Open Label)
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
|---|---|
|
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.
|
|
|
Experimental: Combined DED and Glaucoma Screening
Participants in this arm will have fundus photographs taken for diabetic eye disease screening analyzed by AI for signs of glaucoma.
They will also have intraocular pressure measured.
If glaucoma is suspected, participants will undergo virtual perimetry testing for further assessment.
|
Intraocular pressure measurement by Icare tonometer
Virtual Reality Visual Field Testing by the Olleyes device for participants suspected of having glaucoma
AI analysis of fundus photographs to detect signs of glaucoma, added to AI-based diabetic eye disease screening
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Glaucoma detection
Time Frame: 1 year from initial screening
|
This primary outcome measure will assesses the proportion of participants who receive a glaucoma diagnosis in the combined DED and glaucoma screening group compared to the DED-only screening group.
Glaucoma diagnosis in the intervention group is based on AI-assisted analysis of fundus photography, intraocular pressure, and virtual reality perimetry testing for confirmation.
|
1 year from initial screening
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Cost-Effectiveness of Combined Screening vs. DED-Only Screening
Time Frame: 1 year from initial screening
|
1 year from initial screening
|
|
|
Participant Satisfaction with Screening Process
Time Frame: Day of screening
|
This measure assesses participant satisfaction with the glaucoma screening process.
Satisfaction will be evaluated through a participant satisfaction survey.
|
Day of screening
|
|
Participant knowledge about glaucoma
Time Frame: Day of screening
|
This measure assesses participants' knowledge about glaucoma.
Knowledge will be assessed through a questionnaire (NEI Glaucoma Eye-Q test).
|
Day of screening
|
Collaborators and Investigators
Collaborators
Investigators
- Principal Investigator: David S Friedman, MD, PhD, MPH, Massachusetts Eye and Ear
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Estimated)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- 2024p003709
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
IPD Sharing Time Frame
IPD Sharing Access Criteria
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
product manufactured in and exported from the U.S.
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