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Alzheimer's Disease: Overview01:26

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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ and tau...
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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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Alzheimer disease involves structural changes in the brain that begin long before symptoms appear. The most distinctive features are extracellular neuritic plaques and intracellular neurofibrillary tangles.Neuritic plaques form in the cerebral cortex and around blood vessels. These plaques contain a dense core of beta-amyloid (Aβ)—a toxic protein fragment that clumps outside neurons. The core is surrounded by damaged neuronal extensions, as well as reactive astrocytes and microglia. Abnormal...
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使用大脑皮层复杂性的机器学习模型来诊断阿尔茨海默病.

Shaofan Jiang1,2, Siyu Yang3,4,5, Kaiji Deng1

  • 1Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China.

Frontiers in aging neuroscience
|October 25, 2024
PubMed
概括

使用碎形维度 (FD) 的机器学习模型显示出用于诊断阿尔茨海默病 (AD) 的前景. MoCA + FD模型展示了最高的预测效率,表明AD的潜在非侵入性诊断工具.

关键词:
阿尔茨海默氏症是阿尔茨海默氏症的一种疾病.蒙特利尔的认知评估亚脂蛋白 E 是一种非脂蛋白 E.机器学习是机器学习.磁共振成像技术的使用

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科学领域:

  • 神经科学是一个神经科学.
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 阿尔茨海默病 (AD) 诊断依赖于复杂的评估.
  • 用碎形维度 (FD) 测量的皮层复杂性是AD的潜在生物标志物.
  • 机器学习模型 (MLM) 为疾病诊断提供了新的方法.

研究的目的:

  • 开发和验证使用皮层复杂性 (FD) 诊断AD的MLM.
  • 与其他临床和生物标志物相比,评估基于FD的MLM的诊断性能.
  • 评估MLM在AD诊断中的临床实用性.

主要方法:

  • 从ADNI的296名正常认知 (NC) 和182名AD参与者中,从30个显著改变的皮质区域开发了使用FD的MLM.
  • 内部和外部使用机构队列 (n=66) 验证的模型.
  • 使用接收器操作特征曲线 (AUC) 和决策曲线分析评估模型性能.

主要成果:

  • FD模型在三个队列中预测AD的准确性很好 (AUC:0.842,0.808,0.803).
  • 在所有队列中,MoCA + FD模型实现了最高的预测效率 (AUC:0.951,0.931,0.955).
  • 摩卡+FD模型显示出最大的临床净益.

结论:

  • 基于FD的MLM显示了AD的良好诊断性能.
  • 摩卡+FD模型是AD的高效预测器.
  • 这种方法为AD诊断提供了潜在的非侵入性方法.