NCT07243665

Glaucoma Screening Using Artificial Intelligence Assisted Clinical Model in Singapore's Diabetic Eye Screening Program

A Pragmatic Randomized Controlled Trial of a New Artificial Intelligence-Assisted Clinical Model in Opportunistic Screening for Glaucoma in the Singapore Integrated Diabetic Retinopathy Program

• Status: Recruiting
• Phase: N/A
• Study type: Interventional
• Enrollment: 1,040 (estimated)
• Sponsor: Singapore Eye Research Institute · Academic / Other
• Sex: All
• Age: 21 Years
• Healthy volunteers: Not accepted

Summary

Glaucoma is major cause of irreversible blindness and is characterized by optic nerve damage and visual field loss. Screening for glaucoma is challenging due to lack of a simple, accurate, cost-efficient and standardized process. Artificial intelligence, (AI) especially deep learning (DL) algorithms have potential to automate glaucoma detection, but have to be evaluated in real world settings, before public deployment. This study aims to evaluate the screening accuracy of a DL algorithm for glaucoma detection using colour fundus photographs (CFP) in a pragmatic randomised control trial (RCT). The algorithm will be tested in 1040 eligible patients with diabetes, recruited from the Diabetes \& Metabolism Centre's clinics under the Singapore Integrated Diabetic Retinopathy Program (SiDRP) and randomized to 2 arms: AI-assisted model vs current standard of care (grader assessment). The performance of both arms will be compared to performance of study ophthalmologist in diagnosing glaucoma. We hypothesize that the DL model has better screening performance in detecting glaucoma in the community, compared to the current practice method.

Detailed description

Background: Glaucoma is the leading cause of irreversible blindness worldwide, characterized by optic nerve damage and visual field loss. Screening for glaucoma remains challenging due to lack of a simple, standardized, and cost-effective test. Artificial intelligence (AI), especially deep learning (DL), offers potential to improve and standardize glaucoma detection. However, its performance must be prospectively validated in real-world settings before public deployment. Aim: To evaluate the accuracy and cost-effectiveness of a DL algorithm using colour fundus photographs (CFP) as a clinical decision support tool for glaucoma detection in a real-world setting. Methods: A two-centre, single-blind, pragmatic randomized controlled trial (RCT) will be conducted among 1,040 adults with diabetes recruited from the Diabetes \& Metabolism Centre (DMC) and SingHealth Polyclinics-Bukit Merah under the Singapore Integrated Diabetic Retinopathy Programme (SiDRP). After fundus imaging, participants will be randomized 1:1 to AI-assisted grading or current manual grading by graders at the SiDRP reading center (520 subjects per arm). Diagnostic performance will be compared against the gold-standard glaucoma diagnosis, determined via comprehensive ocular examination including intraocular pressure measurement, visual field testing, optical coherence tomography, and dilated fundus assessment. Cost-effectiveness will be evaluated using a cohort-based Markov model to estimate lifetime costs and incremental cost-effectiveness ratios (ICERs) of the two glaucoma screening strategies. Clinical Significance: Integrating AI into glaucoma screening can address resource constraints and streamline detection. This study will provide real-world evidence on the accuracy and cost-effectiveness of AI-based screening. If validated, it could be integrated into national screening programs to enhance early detection, reduce unnecessary referrals, and prevent avoidable blindness through a cost-efficient, scalable approach.

Conditions

• Glaucoma

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	Artificial Intelligence model to detect glaucoma	A Vision Transformer model to detect glaucoma from fundus photos
OTHER	No intervention	Control group with current practice model by human graders

Timeline

• Start date: 2025-11-17
• Primary completion: 2026-08-01
• Completion: 2027-03-01
• First posted: 2025-11-24
• Last updated: 2026-01-29

Locations

1 site across 1 country: Singapore