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一个集体学习的人工智能模型用于使用OCT检测阿尔茨海默病

An Ran Ran^1,2, Xiaoyan Hu¹, Herbert Y H Hui¹

¹Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China.

Ophthalmology science

|February 20, 2026

概括

这项研究开发了一个使用视网膜OCT扫描来检测阿尔茨海默病 (AD) 痴呆和早期AD的深度学习模型. 该模型显示了在眼科检查期间机会性AD查的前景.

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) 诊断依赖于临床评估和昂贵的生物标志物.
视网膜成像为AD相关的神经退行变化提供了一个非侵入性窗口.
深度学习 (DL) 模型显示了分析复杂成像数据的潜力.

研究的目的:

开发和验证使用光学一致性断层扫描 (OCT) 来检测AD痴呆和早期AD的集合DL模型.
整合多个来自OCT的输入,以提高诊断性能.
评估模型用PET确认的生物标志物对轻度认知障碍 (MCI) 和AD进行分类的能力.

主要方法:

一个回顾性病例控制研究,涉及患有AD痴呆症,MCI和认知正常对照的参与者.
开发了两种基本DL模型 (ONH和斑点),使用了各种OCT成像数据.
通过整合统一分类的基本模型,创建了一个整体模型.
外部验证使用具有PET确认的粉样β状态的独立队列进行.

主要成果:

整体模型在内部验证中实现了0.943的接收器运行特征曲线 (AUROC) 下的区域,用于检测AD-痴呆症.
为了外部验证,该模型显示AD-痴呆症检测的AUROC为0.786和0.795.
该模型在外部队列中检测AD-MCI (基于PET) 时达到约0.79的AUROC.

结论:

拟议的整体DL模型有效地使用OCT成像检测AD-痴呆症和早期AD.
整合多个DL模型和OCT输入可以提高诊断准确度.
这种方法可以在例行眼科检查期间进行机会性AD查.

关键词:

阿尔茨海默氏症的疾病是阿尔茨海默氏症.人工智能的人工智能是人工智能.组合学习学习组合学习其他国家和地区.机会性查是一种机会性查.

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