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

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

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.

Study Overview

• Status: Recruiting
• Conditions: Glaucoma
• Intervention / Treatment: Diagnostic test: Artificial Intelligence model to detect glaucoma; Other: No intervention

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.

• Study Type: Interventional
• Enrollment (Estimated): 1040
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Ching-Yu Cheng, MD, PhD; Phone Number: 65767277; Email: chingyu.cheng@duke-nus.edu.sg
• Study Contact Backup: Name: Lavanya Raghavan, MD; Phone Number: 65767201; Email: raghavan.lavanya@seri.com.sg

Study Locations

• Singapore
  • Singapore
    • Singapore, Singapore, Singapore, 168751
      • Recruiting
      • Singapore National Eye Centre
      • Principal Investigator: Hong Chang Tan
      • Contact: Ching-Yu Cheng, MD, PhD; Phone Number: 65767277
      • Contact: Lavanya Raghavan, MD; Phone Number: 65767201
      • Sub-Investigator: Lavanya Raghavan
      • Principal Investigator: Shiwaza Aminath Moosa

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria: We aim to recruit all eligible patients who attend Singapore General Hospital (SGH) Diabetes & Metabolism Centre's (DMC) clinics and SingHealth Polyclinics (SHP)-Bukit Merah under the Singapore Integrated Diabetic Retinopathy Programme (SiDRP). Patients are eligible for the study if

1. Aged 21 years old and above, with diabetes, including type 1 and type 2,
2. Retinal photos of the patients can be taken with the fundus camera in the clinics, regardless of photos' quality, and
3. They are willing and capable of providing a written informed consent form.

Exclusion Criteria: Patients meeting any of the exclusion criteria will be excluded from participation:

1. Patients who have difficulty in having retinal photos taken or have difficulties in completing the ocular examination protocols according to investigator's decision.

2. Any other contraindication(s) as indicated by the endocrinologists responsible for the patients.

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Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Single

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Active Comparator: Artificial Intelligence Assisted Arm; In this arm, human graders will review fundus photographs for glaucomatous features with the aid of output generated by an AI model trained to detect glaucoma. The AI output will be available during grading to support decision-making.	Diagnostic test: Artificial Intelligence model to detect glaucoma; A Vision Transformer model to detect glaucoma from fundus photos; Other Names: Deep learning model; Vision Transformer; RetiGON
Placebo Comparator: Current practice arm; Graders will assess fundus photographs for glaucoma following standard clinical practice, using a pre-specified and established set of diagnostic criteria without access to AI-generated outputs.	Other: No intervention; Control group with current practice model by human graders; Other Names: Control group; Current practice model

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Evaluation of model performance	To compare the model performance in accuracy, sensitivity, specificity, positive predictive value and negative predictive value between the new AI-assisted clinical model and the current practice model in detecting glaucoma, with reference to the expert panel's standards.	At study completion (after all fundus images have been graded and data collection is finalized; approximately within 12 months of study initiation)

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Evaluation of time efficiency	To compare time efficiency between the AI-assisted clinical model and the current practice model, defined as the total time (in seconds) taken per participant for the entire screening process, from image access to final grading decision, recorded in real time during the grading session.	At study completion (after all fundus images have been graded and data collection is finalized; approximately within 12 months of study initiation)
Evaluation of Grader's Acceptance	To assess graders' acceptance and satisfaction with the AI-assisted clinical model compared to the current practice model in detecting glaucoma. Assessment will be conducted through brief in-task prompts during the grading process and through a structured post-study questionnaire.	At study completion (after all fundus images have been graded and data collection is finalized; approximately within 12 months of study initiation)

Collaborators and Investigators

• Sponsor: Singapore Eye Research Institute
• Collaborators: Singapore General Hospital; SingHealth Polyclinics
• Investigators: Principal Investigator: Ching-Yu Cheng, MD, PhD, Singapore Eye Research Institute

Study record dates

Study Major Dates

• Study Start (Actual): November 17, 2025
• Primary Completion (Estimated): August 1, 2026
• Study Completion (Estimated): March 1, 2027

Study Registration Dates

• First Submitted: November 16, 2025
• First Submitted That Met QC Criteria: November 19, 2025
• First Posted (Actual): November 24, 2025

Study Record Updates

• Last Update Posted (Actual): January 29, 2026
• Last Update Submitted That Met QC Criteria: January 27, 2026
• Last Verified: January 1, 2026

More Information

Terms related to this study

• Keywords: artificial intelligence; screening; Glaucoma; randomised controlled trial; deep learning; fundus photos
• Additional Relevant MeSH Terms: Eye Diseases; Ocular Hypertension; Glaucoma; Investigative Techniques; Epidemiologic Research Design; Epidemiologic Methods; Research Design; Methods; Control Groups
• Other Study ID Numbers: ECOS Ref: 2024-3461; MOH-OFLCG21jun-0003 (Other Grant/Funding Number: National Medical Research Council- Large Collaborative Grant)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: For statistical analysis, for further refinement of the AI model
• IPD Sharing Time Frame: 2028 onwards
• IPD Sharing Access Criteria: Anonymised data only with the permission of the Principal Investigator
• IPD Sharing Supporting Information Type: SAP; ANALYTIC_CODE; CSR

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No