Identification of Memory Loss disease by Ocular Biomarkers using Deep Learning Models

This paper explores the application of deep learning techniques to analyze retinal images obtained through Opti- cal Coherence Tomography Angiography (OCTA) for detecting Alzheimer’s disease (AD). By examining alterations in retinal blood vessel characteristics, such as flow and density, the ap- proach identifies patterns associated with AD. The research builds on existing methodologies and incorporates clinical per- spectives to develop a cost-effective, non-invasive solution for early diagnosis. It also highlights the relevance of specific retinal regions in the detection process, providing valuable information for clinicians and contributing to the understanding of retinal biomarkers related to AD. The findings underscore the potential of this method to enhance both the accuracy and accessibility of diagnosing neurodegenerative conditions.