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Updated: Feb 4, 2026

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深度学习视网膜成像模型识别了没有痴呆症的老年人中脑部健康不佳的情况
Bonnie Yin Ka Lam1,2,3,4,5, An Ran Ran6,7, Yuan Cai1,2,3,4,5
1Division of Neurology, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR.
Cerebral circulation - cognition and behavior
|February 2, 2026
概括
该i-Cog大脑健康工具在健康成年人中确定了年龄较大和视网膜 venular 分支更广泛的特征,这些特征与痴呆风险有关. 这表明它有可能用于早期阿尔茨海默病的检测和风险分层.
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:
- 阿尔茨海默氏症痴呆症的诊断依赖于认知评估.
- 视网膜成像为大脑健康提供了一个非侵入性的窗口.
- 深度学习模型可以分析医疗图像中的复杂模式.
研究的目的:
- 评估i-Cog Brain Health深度学习模型在健康的老年人中检测视网膜变化的能力.
- 评估模型是否能够在非痴呆人口中识别与痴呆风险相关的早期症状.
主要方法:
- 社区主题从BEAT AD计划中招募.
- 使用i-Cog大脑健康模型分析了Fundus照片.
- 对视网膜血管的测量进行了定量评估.
主要成果:
- 在185名受试者中,有29人 (15.68%) 通过i-Cog Brain Health被归类为阳性.
- 积极的分类与年龄较大 (p=0.001) 和较宽的静脉分支宽度 (p=0.008) 相相关.
- 在调整后,静脉分支系数预测了阳性病例 (OR 1.54,p=0.027).
结论:
- i-Cog大脑健康确定了年龄较大和更广泛的静脉分支,这是与痴呆症相关的指标.
- 该模型显示了区分早期视网膜特征与痴呆相关的潜力.
- i-Cog大脑健康可以作为一种有价值的认知衰退风险分层工具.
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