Glaucoma Algorithm Validation Study in African Population - the MAGIC Study

Centro Hospitalar Universitário Lisboa Norte100 enrolled

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

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 Type

INTERVENTIONAL

Allocation

Purpose

DIAGNOSTIC

Masking

NONE

Enrollment

100

Conditions

Eligibility

Sex: ALLMin age: 18 YearsHealthy volunteers:

Medical Language ↔ Plain English

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

Outcomes

Primary Outcomes

Diagnostic agreement between referring decision and reading center decision

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

Time frame: Duration of the study - 3 weeks

Secondary Outcomes

Level of agreement (in %) between AI-risk score and human-based assessment of disease severity

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

Time frame: After the study - 6 months