Glaucoma
Conditions
Keywords
Ophthalmology, Glaucoma, Artificial Intelligence, Retinal Nerve Fiber Layer Optical Texture Analysis, Optic Disc Photography Assessment
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.
Interventions
DIAGNOSTIC_TESTROTA assessment by AI
The RNFL is imaged with OCT for ROTA and the data are analyzed with a deep learning model.
DIAGNOSTIC_TESTOptic disc assessment by AI
The optic disc is imaged with color fundus camera and the data are analyzed with a deep learning model.
Sponsors
Orbis
The University of Hong Kong
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
SCREENING
Masking
NONE
Intervention model description
This is a randomized clinical trial with the primary objective to compare the diagnostic performance of two screening strategies - Retinal nerve fiber layer Optical Texture Analysis (ROTA) assessment by Artificial Intelligence (AI) versus (vs.) optic disc photography assessment by AI or trained graders - for detection of glaucoma in a population-based sample.
Eligibility
Sex/Gender
ALL
Age
50 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* Individuals aged 50 years or above
Exclusion criteria
* Physically incapacitated * Not able to cooperate for clinical examination or optical coherence tomography (OCT) investigation will be excluded
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Diagnostic performance for detection of glaucoma | up to ~1 year | The area under the receiver operating characteristic curve (AUC) for detection of glaucoma |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| 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 |
Other
| Measure | Time frame | Description |
|---|---|---|
| Diagnostic performance for detection of macular diseases | up to ~1 year | The area under the receiver operating characteristic curve (AUC) for detection of macular diseases |
| Incremental cost-effectiveness ratios (ICERs) for population screening of glaucoma and macular diseases | up to ~1 year | ICER for glaucoma and macular diseases screening measured by incremental cost per true positive case detected, incremental cost per incremental QALY |
| The prevalence of macular diseases | up to ~1 year | Proportion of patients with macular diseases |
Countries
Hong Kong
Contacts
Primary ContactAnita Yau
Outcome results
None listed