Glaucoma Algorithm Validation Study in African Population - the MAGIC Study

Официальное название

Validation Study of an Artificial Algorithm for Glaucoma Detection in an African Population

Обзор

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

Подробное описание

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.

Вмешательства

• Диагностический тест 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)

Первичные конечные точки

• Diagnostic agreement between referring decision and reading center decision [Срок оценки: Duration of the study - 3 weeks]

Вторичные конечные точки (1)

• Level of agreement (in %) between AI-risk score and human-based assessment of disease severity [Срок оценки: After the study - 6 months]

Критерии участия

Критерии включения

• subjects age above 18 years old presenting at the Eye Unit
• willingness to sign an informed consent for the screening process

Критерии исключения

• 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

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Да

Дизайн исследования

Распределение

Не применимо

Модель

Одна группа

Маскирование

Открытое

Основная цель

Диагностика

Центры проведения

Список центров уточняется — проверьте первичный протокол.

Идентификаторы

NCT: NCT06552247 · Collaboration Mozambique 2

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

Открыть это исследование на ClinicalTrials.gov ↗