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 noninvasive 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. 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 AI. The study included 87 English‐language publications selected for relevance, and additional references were considered. This article provides an overview of the recent developments and difficulties in using AI and retinal imaging to diagnose cardiovascular diseases. It provides insights for further exploration in this field. Researchers are trying to develop precise disease prognosis patterns in response to the aging population and the growing global burden of CVD. AI and DL are revolutionizing healthcare by potentially diagnosing multiple CVDs from a single retinal image. However, swifter adoption of these technologies in healthcare systems is required.This article is categorized under: Application Areas > Health Care Technologies > Artificial Intelligence

中文翻译：

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人工智能通过视网膜眼底图像评估心血管疾病和风险因素：过去十年回顾


心血管疾病 （CVD） 是全球死亡的主要原因。人工智能 （AI） 方法，尤其是深度学习 （DL）——在分析不同的 CVD 相关主题方面的使用最近呈上升趋势。眼底图像和光学相干断层扫描血管造影 （OCTA） 在视网膜疾病诊断中的应用也已得到广泛研究。为了更好地了解心脏功能并根据微血管特征和功能预测变化，研究人员目前正在探索人工智能与无创视网膜扫描的整合。通过大规模利用人工智能辅助的早期检测和预测心血管疾病，可以减少心血管事件的数量和医疗保健系统的财务压力。使用与心血管疾病和 AI 相关的特定关键字，在各种数据库中进行了全面检索，包括 PubMed、Medline、Google Scholar、Scopus、Web of Sciences、IEEE Xplore 和 ACM 数字图书馆。该研究包括 87 篇根据相关性选择的英语出版物，并考虑了其他参考文献。本文概述了使用 AI 和视网膜成像诊断心血管疾病的最新发展和困难。它为该领域的进一步探索提供了见解。研究人员正在努力开发精确的疾病预后模式，以应对人口老龄化和不断增长的 CVD 全球负担。AI 和 DL 可能从单个视网膜图像中诊断多个 CVD，从而彻底改变医疗保健。然而，需要在医疗保健系统中更快地采用这些技术。本文分类为： 应用领域 > 医疗保健技术 > 人工智能