GlaucomaAIGSNCT07243665
Glaucoma Screening Using Artificial Intelligence Assisted Clinical Model in Singapore's Diabetic Eye Screening Program
A study testing Artificial Intelligence model to detect glaucoma in people with Glaucoma, run by Singapore Eye Research Institute at 1 site in Singapore.
Trial 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. Read more about this study
- Who is it for?
- Ages 21 years and older
- Conditions the study is for
- Glaucoma
- What is being tested?
- Artificial Intelligence model to detect glaucoma
- Could I receive a placebo?
- Yes, some participants may receive a placebo
- How many people
- 1,040 participants
- Where
- 1 siteSingapore
- Study dates
- 17 Nov 2025to Mar 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 21 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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
- Aged 21 years old and above, with diabetes, including type 1 and type 2,
- Retinal photos of the patients can be taken with the fundus camera in the clinics, regardless of photos' quality, and
- 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:
- Patients who have difficulty in having retinal photos taken or have difficulties in completing the ocular examination protocols according to investigator's decision.
- Any other contraindication(s) as indicated by the endocrinologists responsible for the patients. -
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Singapore1 site, 1 recruiting
- Recruiting
Singapore National Eye Centre
Singapore, Singapore, 168751
Ching-Yu Cheng, MD, PhD, 65767277
Lavanya Raghavan, MD, 65767201
About this study
Official title: 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.
Read the 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
- Being tested
- Artificial Intelligence model to detect glaucoma, No intervention
- Keywords
- Glaucoma, deep learning, fundus photos, artificial intelligence, randomised controlled trial, screening
- Medical subject headings
- Glaucoma
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial Intelligence model to detect glaucomaDiagnostic test
Also called Deep learning model, Vision Transformer, RetiGON
A Vision Transformer model to detect glaucoma from fundus photos
No interventionOther
Also called Control group, Current practice model
Control group with current practice model by human graders
| Study group | Role | What they receive |
|---|---|---|
| Artificial Intelligence Assisted Arm | Active comparator | 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. Receives: Diagnostic Test: Artificial Intelligence model to detect glaucoma |
| Current practice arm | Placebo comparator | 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. Receives: Other: No intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: At study completion (after all fundus images have been graded and data collection is finalized; approximately within 12 months of study initiation)
Secondary outcomes (2)
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.
Measured: 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.
Measured: At study completion (after all fundus images have been graded and data collection is finalized; approximately within 12 months of study initiation)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Nov 2025
- Study start17 Nov 2025
- Primary completionAug 2026 (estimated)
- Study completionMar 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for GlaucomaStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 1,040 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Singapore Eye Research Institute
- Sponsor type
- University, hospital or other
- Collaborators
- Singapore General Hospital, SingHealth Polyclinics
- Responsible party
- Sponsor
- Investigators
- Ching-Yu Cheng, MD, PhD, Principal investigator, Singapore Eye Research Institute
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07243665
- Sponsor's protocol ID
- ECOS Ref: 2024-3461
- Other IDs
- MOH-OFLCG21jun-0003
- Acronym
- AIGS
- Status last verified
- Jan 2026
- First submitted
- 16 Nov 2025
- Last updated
- 29 Jan 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: For statistical analysis, for further refinement of the AI model
Study at a glance
From the registry record, last updated by the sponsor on 29 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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
- ClinicalTrials.gov ID
- NCT07243665
- Acronym / sponsor ID
- AIGS / ECOS Ref: 2024-3461
- Conditions
- Glaucoma
- Keywords
- Glaucoma, deep learning, fundus photos, artificial intelligence, randomised controlled trial, screening
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 1,040 participants (estimated)
- Lead sponsor
- Singapore Eye Research InstituteUniversity, hospital or other
- Collaborators
- Singapore General Hospital, SingHealth Polyclinics
- Study start
- 17 Nov 2025 (actual)
- Primary completion
- Aug 2026 (estimated)
- Study completion
- Mar 2027 (estimated)
- First posted
- 24 Nov 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Artificial Intelligence Assisted Arm | Active comparator | Diagnostic Test: Artificial Intelligence model to detect glaucomaIn 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. |
| Current practice arm | Placebo comparator | Other: No interventionGraders 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. |
Who may qualify
- Ages
- 21 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial Intelligence model to detect glaucomaAlso: Deep learning model, Vision Transformer, RetiGONA Vision Transformer model to detect glaucoma from fundus photos | Diagnostic test | Artificial Intelligence Assisted Arm |
| No interventionAlso: Control group, Current practice modelControl group with current practice model by human graders | Other | Current practice arm |
Primary outcome
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)
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.
2 secondary outcomes
Evaluation of time efficiency
Time frame: 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
Time frame: At study completion (after all fundus images have been graded and data collection is finalized; approximately within 12 months of study initiation)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 Jan 2026.
- Individual participant dataSharing planned: Yes. For statistical analysis, for further refinement of the AI model
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Ching-Yu Cheng, MD, PhD principal investigator
Singapore Eye Research Institute
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Artificial Intelligence model to detect glaucoma in people with Glaucoma, run by Singapore Eye Research Institute at 1 site in Singapore.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 29 Jan 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Singapore. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. Some participants in this study may receive a placebo. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Ching-Yu Cheng, MD, PhD (65767277, chingyu.cheng@duke-nus.edu.sg), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the 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)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 29 Jan 2026.