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Alzheimer disease is a chronic, progressive, and irreversible neurodegenerative disorder and the most common cause of dementia in older adults. It leads to gradual neuronal loss, causing cognitive decline, behavioral changes, and loss of functional independence.Risk Factors and EtiologyThe disease is multifactorial. Age is the strongest risk factor, with prevalence doubling every 5 years after age 65. Genetic factors include mutations in genes such as APP, PSEN1, and PSEN2, which are associated...
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相关实验视频

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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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 Ran1,2, Xiaoyan Hu1, Herbert Y H Hui1

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

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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.

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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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.