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利用人工智能识别心血管疾病和中风中的眼科生物标志物：一项叙述性综述。

The use of artificial intelligence to identify ophthalmic biomarkers in cardiovascular disease and stroke: a narrative review.

作者信息

Martins Diogo Gonçalves Dos Santos, Martins Thiago Goncalves Dos Santos, Schor Paulo

机构信息

Department of Ophthalmology, Universidade Federal de São Paulo (UNIFESP), São Paulo (SP), Brazil.

Department of Ophthalmology, Universidade Federal do Rio de Janeiro (UFRJ), Macaé (RJ), Brazil.

出版信息

Sao Paulo Med J. 2025 May 26;143(3):e2023369. doi: 10.1590/1516-3180.2023.0369.11022025. eCollection 2025.

DOI:10.1590/1516-3180.2023.0369.11022025
PMID:40435041
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12105858/
Abstract

BACKGROUND

Cardiovascular disease (CVD) and stroke are among the leading causes of death worldwide.

OBJECTIVE

This article presents a review of the application of artificial intelligence in identifying biomarkers for CVD and stroke.

DESIGN AND SETTING

Narrative review conducted by a research group at the Universidade Federal de São Paulo, São Paulo, Brazil.

METHODS

A literature search was conducted to identify the main applications of artificial intelligence in ophthalmology, using the keywords "artificial intelligence," "prediction," "biomarker," "cardiovascular disease," "retina," and "stroke," covering the period from January 1, 2018, to July 3, 2023. The Medical Literature Analysis and Retrieval System Online (MEDLINE, via PubMed) and the Latin American and Caribbean Literature in Health Sciences (Literatura Latino-Americana e do Caribe em Ciências da Saúde, LILACS, via the Virtual Health Library) were used to identify relevant articles.

RESULTS

A total of 30 references were retrieved, of which 14 were considered eligible for intensive review and critical analysis.

CONCLUSIONS

Artificial intelligence has proven effective in identifying non-invasive biomarkers through the analysis of patients' retinal examinations. These findings contribute to a better understanding of the pathophysiology of CVD and stroke.

摘要

背景

心血管疾病（CVD）和中风是全球主要的死亡原因之一。

目的

本文综述了人工智能在识别心血管疾病和中风生物标志物方面的应用。

设计与背景

由巴西圣保罗联邦大学的一个研究小组进行的叙述性综述。

方法

进行文献检索，以确定人工智能在眼科的主要应用，使用关键词“人工智能”“预测”“生物标志物”“心血管疾病”“视网膜”和“中风”，涵盖2018年1月1日至2023年7月3日期间。使用医学文献分析和检索系统在线数据库（MEDLINE，通过PubMed）以及拉丁美洲和加勒比卫生科学文献数据库（Literatura Latino-Americana e do Caribe em Ciências da Saúde, LILACS，通过虚拟健康图书馆）来识别相关文章。

结果

共检索到30篇参考文献，其中14篇被认为适合进行深入综述和批判性分析。

结论

人工智能已被证明通过分析患者的视网膜检查结果来识别非侵入性生物标志物是有效的。这些发现有助于更好地理解心血管疾病和中风的病理生理学。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3126/12105858/3ce40ac5cc0b/1806-9460-spmj-143-3-e2023369-gf01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3126/12105858/3ce40ac5cc0b/1806-9460-spmj-143-3-e2023369-gf01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3126/12105858/3ce40ac5cc0b/1806-9460-spmj-143-3-e2023369-gf01.jpg

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本文引用的文献

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Eur Heart J Digit Health. 2023 Mar 28;4(3):236-244. doi: 10.1093/ehjdh/ztad023. eCollection 2023 May.
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Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank.基于深度学习的视网膜生物标志物（Reti-CVD）在心血管疾病预测中的验证：来自英国生物库的数据。
BMC Med. 2023 Jan 24;21(1):28. doi: 10.1186/s12916-022-02684-8.
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Retinal age gap as a predictive biomarker of stroke risk.
视网膜年龄差距可作为预测中风风险的生物标志物。
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Retinal photograph-based deep learning predicts biological age, and stratifies morbidity and mortality risk.基于视网膜照片的深度学习可预测生物年龄，并对发病率和死亡率风险进行分层。
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