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基于深度学习的视网膜血管细分的认知衰退的眼部生物标志物

Rui Li¹, Ying Hui¹, Xiaoyue Zhang²

¹Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.

|January 6, 2024

概括

使用深度学习进行视网膜血管分析,显示了识别认知障碍的前景. 这种方法可以检测与认知衰退相关的变化,提供了一个新的查工具.

科学领域:

眼科医生眼科眼科
神经学神经学
人工智能的人工智能

背景情况:

视网膜神经元和血管变化与痴呆症有关.
认知障碍 (CI) 的检测对于早期干预至关重要.

研究的目的:

用NFN+深度学习模型分析视网膜血管特征,以识别认知障碍.
探索视网膜微血管参数与认知功能之间的关联.

主要方法:

利用NFN+深度学习进行视网膜血管细分和测量,对来自KaiLuan研究的908名参与者进行测量.
使用皮尔森相关性评估视网膜微血管参数 (RMP) 和CI之间的相关性.
采用随机森林模型来预测认知衰退,以AUC作为评估标准.

主要成果:

碎形尺寸和全球静脉宽度与认知障碍有显著的相关性 (P<0.05).
认知衰退的关键预测因素包括年龄,BMI,全球静脉宽度,视网膜血管FD和中央视网膜动脉/静脉等价值 (CRAE).
预测模型实现了0.799.9的AUC.

结论:

一个使用视网膜血管特征从 fundus 图像的预测模型显示了高可识别性和认知功能的预测能力.
这种方法可以作为一种有效的认知障碍查方法.

关键词:

认知功能认知功能深度学习的细分化深度学习的细分化基金的摄影工作.视网膜的微血管参数

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