  Background: Cardiovascular diseases (CVDs) are the leading cause of death
globally. The use of artificial intelligence (AI) methods - in particular, deep
learning (DL) - has been on the rise lately for the analysis of different
CVD-related topics. The use of fundus images and optical coherence tomography
angiography (OCTA) in the diagnosis of retinal diseases has also been
extensively studied. To better understand heart function and anticipate changes
based on microvascular characteristics and function, researchers are currently
exploring the integration of AI with non-invasive retinal scanning. There is
great potential to reduce the number of cardiovascular events and the financial
strain on healthcare systems by utilizing AI-assisted early detection and
prediction of cardiovascular diseases on a large scale. Method: A comprehensive
search was conducted across various databases, including PubMed, Medline,
Google Scholar, Scopus, Web of Sciences, IEEE Xplore, and ACM Digital Library,
using specific keywords related to cardiovascular diseases and artificial
intelligence. Results: The study included 87 English-language publications
selected for relevance, and additional references were considered. This paper
provides an overview of the recent developments and difficulties in using
artificial intelligence and retinal imaging to diagnose cardiovascular
diseases. It provides insights for further exploration in this field.
Conclusion: Researchers are trying to develop precise disease prognosis
patterns in response to the aging population and the growing global burden of
CVD. AI and deep learning are revolutionizing healthcare by potentially
diagnosing multiple CVDs from a single retinal image. However, swifter adoption
of these technologies in healthcare systems is required.
