Glaucoma Screening With Artificial Intelligence

Study Overview
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Brief Summary
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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
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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.

Official Title
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Glaucoma Screening With Artificial Intelligence - A Randomized Clinical Trial Comparing Retinal Nerve Fiber Layer Optical Texture Analysis and Optic Disc Photography Assessment

Conditions
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Glaucoma

Intervention / Treatment
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* Diagnostic Test: ROTA assessment by AI
* Diagnostic Test: Optic disc assessment by AI


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 50 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Screening
Allocation: Randomized
Intervention Model: Parallel Assignment
Interventional 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.
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Retinal nerve fiber layer optical texture analysis (ROTA)<br>The RNFL is imaged with OCT for ROTA. | Diagnostic Test: ROTA assessment by AI<br>* The RNFL is imaged with OCT for ROTA and the data are analyzed with a deep learning model.<br>|
| Active Comparator: Optic disc photography<br>The optic disc is imaged with color fundus camera. | Diagnostic Test: Optic disc assessment by AI<br>* The optic disc is imaged with color fundus camera and the data are analyzed with a deep learning model.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Diagnostic performance for detection of glaucoma | The area under the receiver operating characteristic curve (AUC) for detection of glaucoma | up to  1 year | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Incremental cost-effectiveness ratios (ICERs) for population screening of glaucoma | ICER for glaucoma screening measured by incremental cost per true positive case detected, incremental cost per incremental QALY | up to  1 year | 
| The prevalence of glaucoma | Proportion of patients with glaucoma | up to  1 year | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Ophthalmology, Glaucoma, Artificial Intelligence, Retinal Nerve Fiber Layer Optical Texture Analysis, Optic Disc Photography Assessment