Glaucoma Algorithm Validation Study in African Population - the MAGIC Study
- Conditions
- GlaucomaDiabetic Retinopathy
- Interventions
- Diagnostic Test: Fundus Picture AI testing
- Registration Number
- NCT06552247
- Lead Sponsor
- Centro Hospitalar Universitário Lisboa Norte
- Brief Summary
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.
Recruitment & Eligibility
- Status
- NOT_YET_RECRUITING
- Sex
- All
- Target Recruitment
- 100
- subjects age above 18 years old presenting at the Eye Unit
- willingness to sign an informed consent for the screening process
- 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
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- SINGLE_GROUP
- Arm && Interventions
Group Intervention Description AI-based fundus picture screening Fundus Picture AI testing Volunteers will performed a full study visit as part of their regular Ophthalmology assessment. This will include a fundus picture, an Optic-disc entered OCT, a Visual Field exam and a clinical examination by a clinical expert. Fundus picture will be assessed by an AI algorithm (G-Risk) and labelled with referral vs non-referrable and compared with the clinical gold standard
- Primary Outcome Measures
Name Time Method Diagnostic agreement between referring decision and reading center decision Duration of the study - 3 weeks Level of agreement will be done between referring decision and the ground truth as assessed by the reading center (normal, glaucoma suspect; definitive glaucoma). All subjects from both centers (referred and non-referred) will be reviewed.
For a primary outcome analysis, the middle category (glaucoma suspect) will be pooled together with the normal diagnosis
- Secondary Outcome Measures
Name Time Method Level of agreement (in %) between AI-risk score and human-based assessment of disease severity After the study - 6 months Reading center risk score of disease severity (ranked from 0 to 100) will be compared to the AI-based disease score. This will be done separately in each of the 3 categories (normal; glaucoma suspect; glaucoma). Analysis of this score would help refine clinical risk (high risk vs low risk patients) of each category. Exploratory analysis will be made to determine the added value of including this risk score in refining AI-based referral