Development of an equitable AI screening tool for ophthalmic and systemic diseases using retinal imaging and longitudinal cohort data

Project Summary

Vision loss and chronic systemic diseases are major public health concerns, particularly as the population ages. These conditions often go unnoticed until complications occur. Early signs of these diseases can be detected through a non-invasive eye scan that captures images of the back of the eye (the retina). This project aims to create an artificial intelligence (deep learning) model to analyze retinal images and identify signs of eye diseases (like cataract or glaucoma) and systemic diseases (like hypertension or diabetes), enabling early diagnosis.

To ensure the model works well for people of different ages and backgrounds, we will use data from global population studies, including the CLSA. Using CLSA data will help tailor the model to better reflect the Canadian population. This work has the potential to support earlier diagnosis in eye clinics and primary care settings, reduce delays in care and help reduce preventable vision loss and disease progression among older adults.