Jove
Visualize
联系我们

相关概念视频

Alzheimer's Disease: Overview01:26

Alzheimer's Disease: Overview

462
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...
462

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

An integrated predictive model for Alzheimer's disease progression from cognitively normal subjects using generated MRI and interpretable AI.

Scientific reports·2025
Same author

A social image recommendation system based on deep reinforcement learning.

PloS one·2024
Same author

Prediction of Acute Kidney Injury After Cardiac Surgery Using Interpretable Machine Learning.

Anesthesiology and pain medicine·2023
查看所有相关文章
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关实验视频

Updated: Jun 24, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K

脑年龄差距估计使用基于注意力的ResNet方法来检测阿尔茨海默氏症.

Atefe Aghaei1, Mohsen Ebrahimi Moghaddam2,

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

Brain informatics
|June 4, 2024
PubMed
概括

研究人员开发了一种新的3D-Attention-ResNet-SVR模型,通过MRI扫描来估计大脑年龄差距 (BAG). 这种方法在早期发现阿尔茨海默病方面表现有前途,达到92%的准确性.

关键词:
在3D-Resnet中使用3D-Resnet.阿尔茨海默氏症是阿尔茨海默氏症的一种疾病.注意力 注意力 注意力 注意力大脑年龄差距的大脑年龄差距结构性核磁共振成像 (MRI)

更多相关视频

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

7.9K
Examining the Characteristics of Episodic Memory using Event-related Potentials in Patients with Alzheimer's Disease
11:01

Examining the Characteristics of Episodic Memory using Event-related Potentials in Patients with Alzheimer's Disease

Published on: August 30, 2011

13.6K

相关实验视频

Last Updated: Jun 24, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K
Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

7.9K
Examining the Characteristics of Episodic Memory using Event-related Potentials in Patients with Alzheimer's Disease
11:01

Examining the Characteristics of Episodic Memory using Event-related Potentials in Patients with Alzheimer's Disease

Published on: August 30, 2011

13.6K

科学领域:

  • 神经成像是一种神经成像.
  • 人工智能在医学中的应用
  • 神经学 神经学

背景情况:

  • 像阿尔茨海默氏症这样的神经退行性疾病对公共卫生构成重大挑战.
  • 准确的生物标志物对于早期诊断和有效的疾病管理至关重要.
  • 通过MRI估计大脑年龄,为识别神经衰退提供了一个潜在的途径.

研究的目的:

  • 开发和验证一种新的深度学习模型,以准确估计大脑年龄.
  • 计算大脑年龄差距 (BAG) 作为阿尔茨海默病 (AD) 的潜在生物标志物.
  • 评估模型的概括性和性能,以区分认知正常和AD个体.

主要方法:

  • 一种基于注意力的新型ResNet方法,3D-Attention-Resent-SVR被开发用于大脑年龄的估计.
  • 该模型在来自四个公共来源的3844个人的综合数据集上进行了训练和测试.
  • 大脑年龄差距 (BAG) 的计算是为了区分认知正常 (CN) 和阿尔茨海默病 (AD) 群体.

主要成果:

  • 该模型在组合数据集上的大脑年龄差距估计中达到2.05的平均绝对误差 (MAE).
  • 在三个数据集上训练和在一个单独的数据集上测试时,MAE为2.4的优秀概括性被证明.
  • 使用BAG作为唯一的生物标志物,该模型在ADNI数据集上实现了92%的准确性和0.87的AD检测AUC.

结论:

  • 拟议的3D-注意力-光-SVR模型准确地估计了大脑年龄和大脑年龄差距.
  • 大脑年龄差距 (BAG) 作为早期发现阿尔茨海默病的有力生物标志物.
  • 这种方法对神经退行性疾病的早期诊断和监测具有重大潜力.