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.
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.
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.
Parallel
Artificial Intelligence model to detect glaucoma
Diagnostic testA Vision Transformer model to detect glaucoma from fundus photos
No intervention
Other interventionControl group with current practice model by human graders
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.
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.
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.
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Singapore Eye Research Institute
Lead sponsor
Singapore General Hospital
Collaborator
SingHealth Polyclinics
Collaborator
