Early detection of Alzheimer’s disease (AD) permits early intervention of its upstream pathology, which current evidence suggest is more likely to prevent or delay cognitive decline. Yet current diagnostic methods for early AD are not applicable to rapid population-based screening techniques that could contribute to earlier diagnosis. The eye, as an extension of the brain, is affected by AD, and eyes from subjects with AD exhibit a structural pattern that may be used as a “retinal fingerprint” for early detection. In this study, an artificial intelligence (AI) technique will “learn” these retinal patterns using deep learning methods, and its ability to identify eyes from individuals with AD will be evaluated. This retinal fingerprint technique only requires a routine eye-check, and represents an inexpensive, non-invasive, efficient and accessible method for identifying who is most likely to have AD.