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查心房动患者的认知障碍:基于视网膜底部照片的深度学习模型

Zhen Wang^1,2,3, Mingxiao Li^1,2,3, Peng Xia^4,5

¹Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.

Heart rhythm O2

|June 11, 2025

概括

使用视网膜底图像的深度学习模型显示,在心房动 (AF) 患者中查认知障碍 (CI) 有望. 这种方法提供了一种实用的方法,可以在患有AF的人中早期检测CI.

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:

患有心房动 (AF) 的患者面临认知障碍 (CI) 的风险增加.
目前CI的诊断方法复杂,阻碍了AF患者的早期检测和干预.
需要在AF人群中为CI提供可访问的查工具.

研究的目的:

开发和验证深度学习模型,利用视网膜底部照片来早期查AF患者的CI.
评估人工智能驱动的 fundus 图像分析在识别认知障碍方面的有效性.

主要方法:

使用来自中国心房动注册研究 (N=899) 的 fundus 照片开发深度学习模型.
随机将数据分为培训 (80%) 和验证 (20%) 集.
外部验证使用北京眼睛研究的数据.
使用接收器运行特征曲线 (AUROC) 下的面积来评估模型性能.

主要成果:

视觉整体模型,仅使用 fundus 图像,在验证组中实现了 0.855 的 CI 查的 AUROC.
结合 fundus 图像和临床变量的一种多式模式显示了可比性能 (AUROC 0.861).
视觉整体模型在外部测试组中显示出AUROC为0.773,重点关注视网膜血管区域.

结论:

基于视网膜底部图像的视觉整体模型为AF患者的初步CI查提供了实际解决方案.
人工智能对 fundus 图像的分析有可能改善心血管疾病群体的早期CI检测.

关键词:

人工智能的人工智能是人工智能.心房动是一种心房动.认知障碍认知障碍是一种认知障碍.深度学习是一种深度学习.基金的摄影工作.

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