使用视网膜底图像和深度学习预测心血管标志物和疾病:系统范围审查
Livie Yumeng Li1,2, Anders Aasted Isaksen2, Benjamin Lebiecka-Johansen2
1Department of Public Health, Aarhus University, Bartholins Allé 2, 8000 Aarhus C, Denmark.
European heart journal. Digital health
|November 20, 2024
概括
使用视网膜底图像进行深度学习,有望预测心血管风险. 需要进一步的前性研究和与临床因素的整合来加强心血管疾病风险评估.
科学领域:
- 眼科医生 眼科 眼科
- 心脏病学 心脏病学
- 人工智能的人工智能
背景情况:
- 图像分析的深度学习正在迅速发展.
- 视网膜底部图像为心血管健康提供了一个非侵入性的窗口.
- 使用视网膜图像预测心血管风险是一个新兴的研究领域.
研究的目的:
- 对使用视网膜底图像和深度学习进行心血管风险预测的研究进行范围审查.
- 识别和描述当前的研究趋势,方法和这一领域的发现.
主要方法:
- 在2023年11月17日搜索了MEDLINE和Embase数据库.
- 包括使用对视网膜底图像深度学习进行心血管风险/疾病预测的研究.
- 排除了仅依赖于预定义的视网膜图像特征的研究.
- 由两个独立审稿人的截图和全文文章进行了选.
- 使用描述性统计数据呈现研究特征.
主要成果:
- 包括2018年至2023年间发表的24篇文章.
- 大多数研究 (96%) 使用卷积神经网络.
- 大多数是横截面 (96%),有一些后续设计 (33%).
- 9项研究 (38%) 纳入了临床风险因素.
- 四项研究 (17%) 将深度学习预测与临床风险得分进行了比较,其中三项研究显示性能有所改善.
- 对模型的外部验证很少 (21%).
结论:
- 人们对使用视网膜底图像进行心血管风险评估越来越感兴趣.
- 一些研究表明,预测准确度的潜在改进.
- 进一步的研究需要更多的前性研究,与临床风险评分进行比较,并整合传统的风险因素来加强该领域.
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