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

Sponsor
Singapore Eye Research Institute
Study ID
NCT07243665
Status
Recruiting
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Conditions

Eligibility Criteria

Sex
ALL
Age
21 Years - N/A
Healthy Volunteers
Not accepted

Interventions

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

Study Details

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.

Key Dates

First listed
Nov 24, 2025
Start date
Nov 17, 2025
Status verified
Jan 2026
Primary completion
Aug 31, 2026
Completion
Mar 31, 2027

Study Design

Enrollment
1,040 participants (estimated)
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
DIAGNOSTIC

Arms

  • 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.
  • 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.

Primary Outcome Measure

Evaluation of model performance [ Time Frame: At study completion (after all fundus images have been graded and data collection is finalized; approximately within 12 months of study initiation) ]

Central Contacts

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