# Artificial Intelligence in Assessing Cardiovascular Diseases and Risk Factors via Retinal Fundus Images: A Review of the Last Decade

arXiv: https://arxiv.org/abs/2311.07609 · HTML: https://mlsanity.com/paper/2311.07609/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 138 references that are in the map.
- First posted on arXiv 11 Nov 2023.

## Builds on (most cited first, 6 of 6)

- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Predicting Cardiovascular Risk Factors from Retinal Fundus Photographs using Deep Learning](https://mlsanity.com/paper/1708.09843/) (2017)
- [Discharge Summary Hospital Course Summarisation of In Patient Electronic Health Record Text with Clinical Concept Guided Deep Pre-Trained Transformer Models](https://mlsanity.com/paper/2211.07126/) (2022)
- [Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence](https://mlsanity.com/paper/2210.14611/) (2022)

## Built on by (most cited first, 2 of 2)

- [Current and future roles of artificial intelligence in retinopathy of prematurity](https://mlsanity.com/paper/2402.09975/) (2024)
- [Artificial Intelligence and Diabetes Mellitus: An Inside Look Through the Retina](https://mlsanity.com/paper/2402.18600/) (2024)

## Abstract (arXiv)

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.