Recognizing a “Retinal Fingerprint” for Alzheimer’s Using Artificial Intelligence

Carol Y. Cheung, PhD
The Chinese University of Hong Kong (Hong Kong, China)
Year Awarded:
2018
Grant Duration:
July 1, 2018 to June 30, 2022
Disease:
Alzheimer's Disease
Award Amount:
$300,000
Grant Reference ID:
A2018093S
Award Type:
Standard
Award Region:
International
Carol Y. Cheung, PhD

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

Summary

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.

Details

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.

About the Researcher

I completed my BSc and M.Phil degrees from Department of Physics, the University of Hong Kong, and obtained my PhD from School of Metallurgy and Materials Sciences, the University of Birmingham (UK). Following my PhD in 2006, I have trained and been involved in numerous clinical and epidemiological studies using ocular imaging techniques in the Department of Ophthalmology and Visual Sciences, the Chinese University of Hong Kong, and in the Singapore Eye Research Institute, Singapore National Eye Center, for more than 12 years. Currently, I am an Assistant Professor in the Department of Ophthalmology and Visual Sciences, at the Chinese University of Hong Kong. I have conducted a number of major studies testing the validity of different automated computer programs to perform objective assessment of the retinal blood vessels and the optic nerve for evaluation of Alzheimer’s disease, stroke, and major eye diseases. 

Personal Story

As a scientist, my mission is to help people identify their brain and eye problems earlier, via ocular imaging technologies. My main research interest lies in imaging of the eye, based on the concept that the eyes are the “window” to the human circulation and nervous systems. Changes in the retinal blood vessels and the optic nerve mirror parallel changes in the brain and other organs in the body, and can now be imaged easily using new ocular imaging technologies.  These screening techniques have the potential to increase early intervention or treatment in order to prevent dementia and blindness. This could be highly beneficial for individuals, science, and society as a whole. I truly appreciate all the BrightFocus donors and study participants for their support of Alzheimer’s disease research!

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