  Retinal optical coherence tomography (OCT) and optical coherence tomography
angiography (OCTA) are promising tools for the (early) diagnosis of Alzheimer's
disease (AD). These non-invasive imaging techniques are cost-effective and more
accessible than alternative neuroimaging tools. However, interpreting and
classifying multi-slice scans produced by OCT devices is time-consuming and
challenging even for trained practitioners.
  There are surveys on machine learning and deep learning approaches concerning
the automated analysis of OCT scans for various diseases such as glaucoma.
However, the current literature lacks an extensive survey on the diagnosis of
Alzheimer's disease or cognitive impairment using OCT or OCTA. This has
motivated us to do a comprehensive survey aimed at machine/deep learning
scientists or practitioners who require an introduction to the problem. The
paper contains 1) an introduction to the medical background of Alzheimer's
Disease and Cognitive Impairment and their diagnosis using OCT and OCTA imaging
modalities, 2) a review of various technical proposals for the problem and the
sub-problems from an automated analysis perspective, 3) a systematic review of
the recent deep learning studies and available OCT/OCTA datasets directly aimed
at the diagnosis of Alzheimer's Disease and Cognitive Impairment. For the
latter, we used Publish or Perish Software to search for the relevant studies
from various sources such as Scopus, PubMed, and Web of Science. We followed
the PRISMA approach to screen an initial pool of 3073 references and determined
ten relevant studies (N=10, out of 3073) that directly targeted AD diagnosis.
We identified the lack of open OCT/OCTA datasets (about Alzheimer's disease) as
the main issue that is impeding the progress in the field.
