Glaucoma Algorithm Validation Study in African Population - the MAGIC Study
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Fundus Picture AI testing.
- Who it may be relevant to
- Registry conditions: Glaucoma, Diabetic Retinopathy. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Center list to be confirmed — check the primary protocol.
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Validation Study of an Artificial Algorithm for Glaucoma Detection in an African Population
Overview
Artificial Intelligence (AI) algorithms require validation in a variety of populations to ensure widespread clinical applicability. In Ophthalmology, AI algorithms are reaching maturity in diagnosis such as diabetic retinopathy and glaucoma. Higher-at-risk subjects of African descent are nevertheless usually under-represented in training datasets and therefore unclear about representativity. A small scale validation study in consecutive patients in a large Eyesore unit in Mozambique will be performed to determine the diagnostic ability of these AI softwares in this population
Detailed description
Artificial Intelligence (AI) algorithm's are the next frontier in medical management, usually meant to improve diagnostic capabilities and to optimize the existing resources. They are particularly relevant in settings where there is a lack of specialised Human Resources such as physicians.
Ensuring these algorithms can be used in a wide population is therefore crucial to clinical implementation. Validation studies in specific segments of populations are needed to ensure all patients are represented and the results are therefore reliable. Higher-at-risk subjects of African descent are nevertheless usually under-represented in training datasets and therefore unclear about representativity.
A pilot study for validation of an AI algorithm for Glaucoma and Diabetic Retinopathy will be done for the MONA G-RISK® and diabetic retinopathy. Consecutive patients from a large Eye Unit in Mozambique's capital will be screened using these AI algorithms and validated using clinical standard as ground truth.
Interventions
- Diagnostic test Fundus Picture AI testing
G-Risk AI algorithm will assess the optic disc centered fundus picture and determine whether or not there is a need for referrable based on a pre-determined threshold (\>=0.73)
Primary outcome measures
- Diagnostic agreement between referring decision and reading center decision [Time frame: Duration of the study - 3 weeks]
Secondary outcome measures (1)
- Level of agreement (in %) between AI-risk score and human-based assessment of disease severity [Time frame: After the study - 6 months]
Eligibility criteria
Inclusion criteria
- subjects age above 18 years old presenting at the Eye Unit
- willingness to sign an informed consent for the screening process
Exclusion criteria
- none
- Poor quality in screening image will be included in the intention to treat analysis, but excluded from the diagnostic comparator outcome.
- Patients with a known glaucoma diagnosis will not be excluded from the screening
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Allocation
- N/A
- Model
- Single group
- Masking
- Open label
- Primary purpose
- Diagnostic
Study locations
Center list to be confirmed — check the primary protocol.
Identifiers
NCT: NCT06552247 · Collaboration Mozambique 2