Glaucoma Screening With Artificial Intelligence

September 19, 2023 updated by: Professor Christopher K.S. Leung, The University of Hong Kong

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

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

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.

Study Type

Interventional

Enrollment (Estimated)

3175

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • Hong Kong
      • Aberdeen, Hong Kong
        • Recruiting
        • Southern District Wah Kwai Community Centre
        • Contact:
      • Kwun Tong, Hong Kong
        • Recruiting
        • Kwun Tong District Health Centre
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Description

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

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

Arms and Interventions

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

What is the study measuring?

Primary Outcome Measures

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

Other Outcome Measures

Outcome Measure
Measure Description
Time Frame
Diagnostic performance for detection of macular diseases
Time Frame: up to ~1 year
The area under the receiver operating characteristic curve (AUC) for detection of macular diseases
up to ~1 year
The prevalence of macular diseases
Time Frame: up to ~1 year
Proportion of patients with macular diseases
up to ~1 year
Incremental cost-effectiveness ratios (ICERs) for population screening of glaucoma and macular diseases
Time Frame: 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
up to ~1 year

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

The University of Hong Kong

Collaborators

Orbis

Investigators

  • Principal Investigator: Christopher Leung, The University of Hong Kong

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Helpful Links

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

August 26, 2023

Primary Completion (Estimated)

August 25, 2024

Study Completion (Estimated)

February 25, 2025

Study Registration Dates

First Submitted

August 21, 2023

First Submitted That Met QC Criteria

August 21, 2023

First Posted (Actual)

August 25, 2023

Study Record Updates

Last Update Posted (Actual)

September 21, 2023

Last Update Submitted That Met QC Criteria

September 19, 2023

Last Verified

September 1, 2023

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • H012_Protocol_Glaucoma

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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