Li Livie Yumeng, Isaksen Anders Aasted, Lebiecka-Johansen Benjamin, Funck Kristian, Thambawita Vajira, Byberg Stine, Andersen Tue Helms, Norgaard Ole, Hulman Adam
Department of Public Health, Aarhus University, Bartholins Allé 2, 8000 Aarhus C, Denmark.
Steno Diabetes Center Aarhus, Aarhus University Hospital, Palle Juul-Jensens Boulevard 11, 8200 Aarhus N, Denmark.
Eur Heart J Digit Health. 2024 Sep 10;5(6):660-669. doi: 10.1093/ehjdh/ztae068. eCollection 2024 Nov.
Rapid development in deep learning for image analysis inspired studies to focus on predicting cardiovascular risk using retinal fundus images. This scoping review aimed to identify and describe studies using retinal fundus images and deep learning to predict cardiovascular risk markers and diseases. We searched MEDLINE and Embase on 17 November 2023. Abstracts and relevant full-text articles were independently screened by two reviewers. We included studies that used deep learning for the analysis of retinal fundus images to predict cardiovascular risk markers or cardiovascular diseases (CVDs) and excluded studies only using predefined characteristics of retinal fundus images. Study characteristics were presented using descriptive statistics. We included 24 articles published between 2018 and 2023. Among these, 23 (96%) were cross-sectional studies and eight (33%) were follow-up studies with clinical CVD outcomes. Seven studies included a combination of both designs. Most studies (96%) used convolutional neural networks to process images. We found nine (38%) studies that incorporated clinical risk factors in the prediction and four (17%) that compared the results to commonly used clinical risk scores in a prospective setting. Three of these reported improved discriminative performance. External validation of models was rare (21%). There is increasing interest in using retinal fundus images in cardiovascular risk assessment with some studies demonstrating some improvements in prediction. However, more prospective studies, comparisons of results to clinical risk scores, and models augmented with traditional risk factors can strengthen further research in the field.
深度学习在图像分析领域的快速发展促使研究聚焦于利用视网膜眼底图像预测心血管风险。本综述旨在识别和描述使用视网膜眼底图像及深度学习来预测心血管风险标志物和疾病的研究。我们于2023年11月17日检索了MEDLINE和Embase数据库。两位评审员独立筛选摘要和相关全文文章。我们纳入了使用深度学习分析视网膜眼底图像以预测心血管风险标志物或心血管疾病（CVD）的研究，并排除了仅使用视网膜眼底图像预定义特征的研究。使用描述性统计呈现研究特征。我们纳入了2018年至2023年间发表的24篇文章。其中，23篇（96%）为横断面研究，8篇（33%）为有临床CVD结局的随访研究。7项研究包含了两种设计的组合。大多数研究（96%）使用卷积神经网络处理图像。我们发现9项（38%）研究在预测中纳入了临床风险因素，4项（17%）研究在前瞻性研究中将结果与常用临床风险评分进行了比较。其中3项研究报告了判别性能的改善。模型的外部验证很少（21%）。在心血管风险评估中使用视网膜眼底图像的兴趣日益增加，一些研究表明在预测方面有一定改进。然而，更多的前瞻性研究、将结果与临床风险评分进行比较以及用传统风险因素增强模型，可加强该领域的进一步研究。
