Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China.
Curr Opin Ophthalmol. 2022 Sep 1;33(5):440-446. doi: 10.1097/ICU.0000000000000886. Epub 2022 Jul 19.
Retinal microvasculature assessment has shown promise to enhance cardiovascular disease (CVD) risk stratification. Integrating artificial intelligence into retinal microvasculature analysis may increase the screening capacity of CVD risks compared with risk score calculation through blood-taking. This review summarizes recent advancements in artificial intelligence based retinal photograph analysis for CVD prediction, and suggests challenges and future prospects for translation into a clinical setting.
Artificial intelligence based retinal microvasculature analyses potentially predict CVD risk factors (e.g. blood pressure, diabetes), direct CVD events (e.g. CVD mortality), retinal features (e.g. retinal vessel calibre) and CVD biomarkers (e.g. coronary artery calcium score). However, challenges such as handling photographs with concurrent retinal diseases, limited diverse data from other populations or clinical settings, insufficient interpretability and generalizability, concerns on cost-effectiveness and social acceptance may impede the dissemination of these artificial intelligence algorithms into clinical practice.
Artificial intelligence based retinal microvasculature analysis may supplement existing CVD risk stratification approach. Although technical and socioeconomic challenges remain, we envision artificial intelligence based microvasculature analysis to have major clinical and research impacts in the future, through screening for high-risk individuals especially in less-developed areas and identifying new retinal biomarkers for CVD research.
视网膜微血管评估显示出增强心血管疾病 (CVD) 风险分层的潜力。与通过采血进行风险评分计算相比，将人工智能整合到视网膜微血管分析中可能会增加 CVD 风险的筛查能力。本综述总结了基于人工智能的视网膜照片分析在 CVD 预测方面的最新进展，并提出了将其转化为临床环境的挑战和未来前景。
基于人工智能的视网膜微血管分析可能预测 CVD 风险因素（例如血压、糖尿病）、直接 CVD 事件（例如 CVD 死亡率）、视网膜特征（例如视网膜血管直径）和 CVD 生物标志物（例如冠状动脉钙评分）。然而，处理同时存在视网膜疾病的照片、来自其他人群或临床环境的数据有限且多样化、可解释性和通用性不足、对成本效益和社会接受度的担忧等挑战可能会阻碍这些人工智能算法在临床实践中的传播。
基于人工智能的视网膜微血管分析可能补充现有的 CVD 风险分层方法。尽管仍存在技术和社会经济方面的挑战，但我们预计基于人工智能的微血管分析在未来会对临床和研究产生重大影响，特别是在欠发达地区通过筛查高危人群和确定 CVD 研究的新视网膜生物标志物。
