生物标志物 生物标志物
Anran Ran1,2, Yuen Tung Ng3,4,5,6,7, Bonnie Y K Lam3,4,5,6,7
1Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, NA, Hong Kong.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 25, 2025
概括
该RetinAD深度学习模型识别了老年,非痴呆症的患有视网膜微血管病变的个体,这表明它可以预测未来的阿尔茨海默病 (AD) 风险.
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:
- 阿尔茨海默病 (AD) 与视网膜变化有关.
- 深度学习模型RetinAD使用视网膜图像将AD痴呆与健康对照区分开来.
- 视网膜微血管病变可能先于AD的认知症状.
研究的目的:
- 调查RetinAD是否可以检测非痴呆症老年人视网膜微血管病变.
- 为了比较视网膜血管网络测量在这个队列中的RetinAD分类的"阳性"和"阴性"病例之间.
主要方法:
- 从BEAT AD计划中招募了187名具有主观认知衰退 (SCD) 的非痴呆老年人 (59-80岁).
- 利用RetinAD将受试者分类为AD相关的视网膜变化"阳性"或"阴性".
- 使用新加坡I血管评估 (SIVA) 软件量化视网膜血管网络.
主要成果:
- 15.5%的受试者被归类为RetinAD"阳性".
- "阳性"受试者年龄较大,动脉和静脉分支系数较高.
- 在调整年龄和血压后,静脉分支系数仍然显著.
结论:
- 在RetinAD中,不患痴呆症的老年人被发现患有较差的视网膜微血管病变和"生物学上较老"的大脑.
- 这些发现表明RetinAD有潜力识别未来患AD痴呆症风险的个体.
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