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

NCT ID: NCT07243665

Last Updated: 2025-11-24

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Clinical Phase: NA
• Total Enrollment: 1040 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2025-11-30
• Study Completion Date: 2027-03-31

Brief 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.

Conditions

Glaucoma

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: SINGLE

Study Groups

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.

Group Type: ACTIVE_COMPARATOR

Artificial Intelligence model to detect glaucoma

Intervention Type: DIAGNOSTIC_TEST

A Vision Transformer model to detect glaucoma from fundus photos

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.

Group Type: PLACEBO_COMPARATOR

No intervention

Intervention Type: OTHER

Control group with current practice model by human graders

Interventions

Artificial Intelligence model to detect glaucoma

A Vision Transformer model to detect glaucoma from fundus photos

Intervention Type: DIAGNOSTIC_TEST

No intervention

Control group with current practice model by human graders

Intervention Type: OTHER

Other Intervention Names

Deep learning model; Vision Transformer; RetiGON; Control group; Current practice model

Eligibility Criteria

Inclusion Criteria

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

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.

-

• Minimum Eligible Age: 21 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Singapore General Hospital

OTHER

Sponsor Role: collaborator

SingHealth Polyclinics

OTHER

Sponsor Role: collaborator

Singapore Eye Research Institute

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Ching-Yu Cheng, MD, PhD

Role: PRINCIPAL_INVESTIGATOR

Singapore Eye Research Institute

Locations

Singapore National Eye Centre

Singapore, Singapore, Singapore

Countries

Singapore

Central Contacts

Ching-Yu Cheng, MD, PhD

Role: CONTACT

chingyu.cheng@duke-nus.edu.sg

65767277

Lavanya Raghavan, MD

Role: CONTACT

raghavan.lavanya@seri.com.sg

65767201

Other Identifiers

MOH-OFLCG21jun-0003

Identifier Type: OTHER_GRANT

Identifier Source: secondary_id

ECOS Ref: 2024-3461

Identifier Type: -

Identifier Source: org_study_id