Artificial Intelligence model to detect glaucomaNo intervention
Artificial Intelligence model to detect glaucoma: A Vision Transformer model to detect glaucoma from fundus photos
No intervention: Control group with current practice model by human graders
Study 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.
Eligibility
Sex
ALL
Min age
21 Years
Max age
—
Healthy volunteers
No
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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Primary outcome measure(s)
Evaluation of model performance — 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.
Trial sites (1)
Facility
City
Region
Status
Singapore National Eye Centre
Singapore
Singapore
Recruiting
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This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
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