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Alzheimer's Disease: Overview01:26

Alzheimer's Disease: Overview

432
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...
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Alzheimer's Disease: Treatment01:22

Alzheimer's Disease: Treatment

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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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Dementia01:30

Dementia

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Dementia is a collective term for cognitive disorders primarily affecting memory, thinking, and reasoning. It is not a specific disease but a syndrome, with Alzheimer's disease being the most common cause, accounting for approximately 60-80% of cases. Other types include vascular dementia, Lewy body dementia, and frontotemporal dementia. Dementia affects millions worldwide, particularly older adults, though it is not a normal part of aging.
The progression of dementia is generally gradual....
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Updated: May 28, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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用哈里斯·霍克斯优化 (HHO) 和基于深度学习的方法预测阿尔茨海默病的方法,使用MLP-LSTM混合网络.

Raheleh Ghadami1, Javad Rahebi2

  • 1Department of Computer Engineering, Istanbul Topkapi University, 34662 Istanbul, Türkiye.

Diagnostics (Basel, Switzerland)
|February 13, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种先进的机器学习 (ML) 和深度学习 (DL) 方法,用于使用MRI扫描准确诊断阿尔茨海默病. 综合方法显著提高了分类准确性,有助于早期检测和干预.

关键词:
阿尔茨海默氏症是阿尔茨海默氏症的一种疾病.哈里斯·霍克斯优化 (HHO) 算法LSTM神经网络是一个神经网络.卷积神经网络 (CNN) 是一种神经网络.磁共振成像 (MRI) 是一种磁共振成像.

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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) 是一种进展性神经退行性疾病,其特点是认知能力下降.
  • 通过MRI早期诊断对于有效的医疗干预至关重要.
  • 目前的ML/DL方法在MRI中准确区分AD和健康状态时面临挑战.

研究的目的:

  • 开发一种集成的ML/DL方法,结合群体智能来增强AD分类.
  • 提高使用MRI数据区分健康和AD患者的准确性.

主要方法:

  • 从使用卷积神经网络 (CNN) 和灰色水平共发生矩阵 (GLCM) 的AD相关的MRI图像中提取特征.
  • 使用哈里斯·霍克斯优化 (HHO) 算法进行特征选择.
  • 在ADNI数据集上使用多层感知器 (MLP) 和长短期记忆 (LSTM) 网络进行分类.

主要成果:

  • 拟议的综合方法实现了97.59%的高分类精度.
  • 在阿尔茨海默病诊断中获得了卓越的灵敏度 (97.41%) 和精度 (97.25%).
  • 在诊断性能方面表现优于VGG16,GLCM和ResNet-50等既有模型.

结论:

  • 集成的群体智能,ML和DL方法在改善AD诊断方面表现出显著的有效性.
  • 先进的特征提取和选择技术是提高医学成像诊断精度的关键.
  • 这项研究强调了集成人工智能方法在神经成像诊断工具的发展方面的潜力.