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: 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.

• 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.

Deidentified study data

After study completion

Data sharing will require adherence to Mass General Brigham institutional and IRB policies. External researchers will need to contact the Principal Investigator to request data and complete a Data Use Agreement. Only deidentified data will be shared.