Validation Study of an Artificial Algorithm for Glaucoma Detection in an African Population
Trial Snapshot
- Phase
- Not Applicable
- Status
- Not yet recruiting
- Enrollment
- 100
- Primary Endpoint
- Diagnostic agreement between referring decision and reading center decision
Study Overview
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.
Study Design
- Study Type
- Interventional
- Allocation
- Na
- Intervention Model
- Single Group
- Primary Purpose
- Diagnostic
- Masking
- None
Eligibility Criteria
- Ages
- 18 Years to — (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- Yes
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
- •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
Arms & Interventions
AI-based fundus picture screening
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
Intervention: Fundus Picture AI testing (Diagnostic Test)
Outcomes
Primary Outcomes
Diagnostic agreement between referring decision and reading center decision
Time Frame: 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 Outcomes
- Level of agreement (in %) between AI-risk score and human-based assessment of disease severity(After the study - 6 months)
Investigators
Luis Abegao Pinto
Co-principal investigator
Centro Hospitalar Universitário Lisboa Norte