Modular machine learning for Alzheimer's disease classification from retinal vasculature


Alzheimer’s disease is the leading cause of dementia. The long progression period in Alzheimer’s disease provides a possibility for patients to get early treatment by having routine screenings. However, current clinical diagnostic imaging tools do not meet the specific requirements for screening procedures due to high cost and limited availability. In this work, we took the initiative to evaluate the retina, especially the retinal vasculature, as an alternative for conducting screenings for dementia patients caused by Alzheimer’s disease. Highly modular machine learning techniques were employed throughout the whole pipeline. Utilizing data from the UK Biobank, the pipeline achieved an average classification accuracy of 82.44%. Besides the high classification accuracy, we also added a saliency analysis to strengthen this pipeline’s interpretability. The saliency analysis indicated that within retinal images, small vessels carry more information for diagnosing Alzheimer’s diseases, which aligns with related studies.

Jan 18, 2022 3:00 PM
Online (requires registration)

Professor Ruogu Fang of the University of Florida will present as part of the University of Oxford Department of Psychiatry’s Artificial Intelligence for Mental Health Seminar Series on Tuesday 18th January 2022 at 15:00 (GMT / UK time).

Please contact Andrey Kormilitzin to register and recieve a link to the seminar.

Andrey Kormilitzin
Andrey Kormilitzin
Senior Researcher

My research is centred around translating advances in mathematics, statistical machine learning and deep learning to address challenges involved in learning, inference and ethical decision making using complex biomedical and health data.