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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.
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Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
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从认知上正常的受试者对阿尔茨海默病进展的综合预测模型使用生成的MRI和可解释的AI.

Atefe Aghaei1, Mohsen Ebrahimi Moghaddam2

  • 1Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran.

Scientific reports
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概括

这项研究引入了一个新的AI框架来预测阿尔茨海默病 (AD) 从认知正常阶段的进展. 该方法可以准确预测多达10年的AD,有助于早期诊断和干预.

关键词:
预测阿尔茨海默氏症的进展情况.自动ROI提取自动ROI提取组合转移学习学习这就是为什么MRI是MRI.可能性的概率.维生素-GANAN 是一个

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科学领域:

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

背景情况:

  • 阿尔茨海默病 (AD) 是一种具有早期认知变化的渐进性神经退行性疾病.
  • 早期诊断AD对于及时干预和管理至关重要.
  • 从认知正常 (CN) 阶段预测AD进展仍然是一个挑战,因为纵向数据有限.

研究的目的:

  • 开发一个综合的人工智能框架,从认知正常阶段预测阿尔茨海默病的进展.
  • 为了利用集体转移学习,生成建模和ROI提取用于AD预测.
  • 通过确定参与疾病进展的关键大脑区域来提高模型的透明度.

主要方法:

  • 使用了阿尔茨海默病神经成像计划 (ADNI) 数据集.
  • 采用了三阶段的过程:集体转移学习用于CN到MCI过渡概率估计,ViT-GAN用于模拟未来的MRI图像,3D CNN与同位素回归用于AD预测.
  • 应用Grad-CAM用于解释关键感兴趣区域 (ROI).

主要成果:

  • 在10年内预测CN转换为AD的过程中,获得了高精度 (0.85) 和F1得分 (0.86).
  • 成功生成模拟疾病进展的合成MRI图像.
  • 通过ROI解释识别了与AD进展相关的关键大脑区域.

结论:

  • 拟议的综合框架显示了早期阿尔茨海默病诊断的巨大潜力.
  • 该方法通过生成合成图像来解决数据的局限性,并提高可解释性.
  • 为阿尔茨海默病管理中个性化干预策略提供了一个有前途的工具.