Could AI help clinicians to differentiate neurodegenerative disease from retinal scans?

University of Waterloo researchers have suggested that applying machine learning to retinal scans could help to differentiate between different forms of brain disease.

Writing in Alzheimer’s and Dementia: The Journal of the Alzheimer’s Association, scientists note that polarised light interacts differently with two types of retinal deposits associated with different neurogenerative diseases.

Amyloid beta deposits are associated with Alzheimer’s disease while deposits of TDP-43 are linked to frontotemporal lobular dementia (FTLD) and amyotrophic lateral sclerosis (ALS).

“The deposits’ polarised light interactions are used in machine learning to classify the deposits,” the researchers highlighted.

University of Waterloo professor emeritus of physics and optometry, Dr Melanie Campbell, described the findings as “a major step toward earlier and more accurate diagnosis.”

“Right now, FTLD and ALS are diagnosed only after symptoms appear, which often means the disease is already advanced. Being able to detect these conditions earlier could transform how we treat them,” she said.

Campbell shared that a fast and accessible diagnostic test could make a significant difference for patients and their families.

“We hope that within a few years, this technology will evolve into a simple eye test capable of detecting and distinguishing multiple brain diseases, giving patients in smaller, underserved communities access to this type of testing,” she said.