Recognition of Retinal Fingerprint for Alzheimer’s Disease using Deep Learning Approach

Project Summary

My research is directed towards developing more targeted and effective screening strategies for early detection of brain diseases and prevention of blindness, using state-of-the-art ocular imaging technology. Such screening techniques hold immense potential to lead to impactful benefits for both science and society.

Using retinal imaging to study Alzheimer’s diseases (AD) is one of my major research interests. Early detection of AD provides a critical opportunity to prevent or delay cognitive decline, yet current diagnostic modalities for early AD are not applicable to population-based screening.

In this project, an artificial intelligence (AI) modality will be trained with deep learning algorithms to recognize the “retinal fingerprint” of AD, ie, a specific pattern of retinal changes secondary to AD pathology. The trained AI can then identify individuals in an outpatient or community setting who are more likely to have AD, based on features and patterns from retinal images.

Early identification of this high-risk group of individuals not only allows medial intervention before irreversible neurodegeneration, but also deepens our understanding of upstream pathology of AD. In the future, we can easily know our approximate risk of AD by receiving a simple eye-check and completing a questionnaire that records personal demographic information. The individuals “tagged” by this initial screening can then have their diagnosis confirmed using more expensive and invasive investigations (such as amyloid PET imaging). They will subsequently be in a better position to receive early medical intervention to prevent cognitive decline.