Glaucoma Screening With Artificial Intelligence
- Conditions
- Glaucoma
- Interventions
- Diagnostic Test: ROTA assessment by AIDiagnostic Test: Optic disc assessment by AI
- Registration Number
- NCT06012058
- Lead Sponsor
- The University of Hong Kong
- Brief Summary
This randomized clinical trial aims to compare the diagnostic performance of two AI-enabled screening strategies - ROTA (RNFL optical texture analysis) assessment versus optic disc photography - in detecting glaucoma within a population-based sample. Secondary objectives are to (1) compare the diagnostic performance of ROTA AI assessment versus OCT RNFL thickness assessment by AI, and ROTA AI assessment versus OCT RNFL thickness assessment by trained graders, (2) investigate the cost-effectiveness of AI ROTA assessment for glaucoma screening, and (3) estimate the prevalence of glaucoma in Hong Kong.
- Detailed Description
Glaucoma is the leading cause of irreversible blindness affecting 76 million patients worldwide in 2020. Characterized by progressive degeneration of the optic nerve, early detection of disease deterioration with timely intervention is critical to prevent progressive loss in vision. In the 5th World Glaucoma Association Consensus Meeting, a diverse and representative group of glaucoma clinicians and scientists deliberated on the value and methods of glaucoma screening. Whereas it has been recognized that early detection of glaucoma for treatment is beneficial to preserve the quality of vision and quality of life as glaucoma treatments are often effective, easy to use and well tolerated, the optimal screening strategy for glaucoma has not yet been determined.
ROTA (Retinal Nerve Fiber Layer Optical Texture Analysis) is a patented algorithm designed to detect axonal fiber bundle loss in glaucoma. Unlike conventional Optical Coherence Tomography (OCT) analysis, ROTA uses non-linear transformation to reveal the optical textures and trajectories of axonal fiber bundles, allowing for intuitive and reliable recognition of RNFL abnormalities without the need for normative databases. It can be applied across different OCT models and is particularly effective at detecting focal RNFL defects in early glaucoma and varying degrees of RNFL damage in end-stage glaucoma. The proposed study will address whether the application AI on ROTA is feasible and cost-effective in the setting of glaucoma screening, and whether ROTA would outperform optic disc photography and OCT RNFL thickness assessment.
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- All
- Target Recruitment
- 3175
- Individuals aged 50 years or above
- Physically incapacitated
- Not able to cooperate for clinical examination or optical coherence tomography (OCT) investigation will be excluded
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- PARALLEL
- Arm && Interventions
Group Intervention Description Retinal nerve fiber layer optical texture analysis (ROTA) ROTA assessment by AI The RNFL is imaged with OCT for ROTA. Optic disc photography Optic disc assessment by AI The optic disc is imaged with color fundus camera.
- Primary Outcome Measures
Name Time Method Diagnostic performance for detection of glaucoma up to ~1 year The area under the receiver operating characteristic curve (AUC) for detection of glaucoma
- Secondary Outcome Measures
Name Time Method Incremental cost-effectiveness ratios (ICERs) for population screening of glaucoma up to ~1 year ICER for glaucoma screening measured by incremental cost per true positive case detected, incremental cost per incremental QALY
The prevalence of glaucoma up to ~1 year Proportion of patients with glaucoma
Trial Locations
- Locations (2)
Kwun Tong District Health Centre
🇭🇰Kwun Tong, Hong Kong
Southern District Wah Kwai Community Centre
🇭🇰Aberdeen, Hong Kong