Jove
Visualize
联系我们
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

相关概念视频

Dementia01:30

Dementia

486
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....
486
Alzheimer's Disease: Overview01:26

Alzheimer's Disease: Overview

1.6K
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...
1.6K
Alzheimer's Disease: Treatment01:22

Alzheimer's Disease: Treatment

752
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...
752

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 explainable meta-learned hybrid CNN-transformer model with dual attention for leukemia diagnosis from peripheral blood smears.

Scientific reports·2026
Same author

Complication Risk Classification in Children and Adolescents With Type 1 Diabetes: Interpretable Machine Learning Study Based on Saudi Clinical Guidelines.

JMIR formative research·2026
Same author

Enhancing the Diagnosis of Behçet's Disease Using Machine Learning: A Comparative Study on Clinical Data From Saudi Arabia.

International journal of telemedicine and applications·2026
Same author

Comparative analysis of multiple deep learning models with mitigation-driven approaches for enhanced Alzheimer's disease classification.

Scientific reports·2025
Same author

Dementia medications and the use of psychotropic drugs: A cross-sectional study in Saudi healthcare.

Journal of Alzheimer's disease : JAD·2025
Same author

CausalFormer-HMC: a hybrid memory-driven transformer with causal reasoning and counterfactual explainability for leukemia diagnosis.

Frontiers in cell and developmental biology·2025
Same journal

Synaptic micromechanics and brain softening as a mechanobiological hypothesis for Alzheimer's disease.

Frontiers in neuroscience·2026
Same journal

The relationship between healthy sleep patterns and the risk of scoliosis: a large prospective cohort study.

Frontiers in neuroscience·2026
Same journal

Dynamic functional reorganization in post-stroke aphasia: a state-of-the-art fMRI review from disease evolution to intervention.

Frontiers in neuroscience·2026
Same journal

Correction: Case Report: A possible novel adult-onset, progressive MAO-A hypofunction.

Frontiers in neuroscience·2026
Same journal

Respiratory modulation of neurophysiology and symptoms in athletes with sports-related concussion: a randomized crossover trial.

Frontiers in neuroscience·2026
Same journal

Impact of C-reactive protein-triglyceride-glucose and systemic immune-inflammation indices on obstructive sleep apnea in older adults with depression.

Frontiers in neuroscience·2026
查看所有相关文章

相关实验视频

Updated: Jan 9, 2026

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

1.7K

多模型深度学习用于痴呆症检测:解决数据和模型的局限性.

Areej Y Bayahya1,2, Fares Jammal3, Haneen Banjar1,4,5,6

  • 1Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

Frontiers in neuroscience
|December 11, 2025
PubMed
概括
此摘要是机器生成的。

这项研究评估了使用结构性MRI扫描诊断痴呆症的深度学习模型. 多模式注意力和3D-CNN模型显示出最佳表现,但对于准确的分类,在精度和概括方面仍然存在挑战.

关键词:
在3D-CNN中.在这里,我们可以看到AIAIAI.美国有线电视新闻网 (CNN)这里是ViT ViT ViT在XAI,XAI就是XAI.美国Caps网络 Caps网络深度神经网络是一个神经网络.痴呆症 痴呆症是一种痴呆症.

更多相关视频

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.9K
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

1.4K

相关实验视频

Last Updated: Jan 9, 2026

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

1.7K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.9K
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

1.4K

科学领域:

  • 人工智能的人工智能
  • 医学成像分析 医学成像分析
  • 神经科学是一个神经科学.

背景情况:

  • 深度神经网络 (DNN) 已经在医疗成像方面取得了先进的发展,特别是在结构性MRI (sMRI) 分类方面.
  • 目前的DNN模型在痴呆症诊断的预处理和特征提取方面存在局限性.
  • 这项研究通过评估各种痴呆症分类架构来解决这些挑战.

研究的目的:

  • 用sMRI数据评估多种深度学习架构的性能,以对痴呆症,轻度认知障碍 (MCI) 和健康对照进行分类.
  • 根据准确性,特异性和敏感性,确定痴呆症诊断的最有效模型.

主要方法:

  • 评估了八个预训练的卷积神经网络 (CNN),视觉变换器 (ViT),多式联络注意力模型和囊网络 (CapsNet).
  • 使用ADNI的平衡数据集,包括每班10,000次培训,3000次验证和850次测试图像 (痴呆症,MCI,健康对照).
  • 使用sMRI扫描的2D切片进行分类,测量精度,特异性和灵敏度.

主要成果:

  • 3D-CNN和多式联络注意力模型实现了最高的性能 (例如,多式联络注意力的精度为86%,灵敏度为86%).
  • ViT和CapsNet对阿尔茨海默病 (AD) 的敏感度为100%,但精度低 (AD为43%,其他为0%),表明了阶级失衡问题.
  • 所有模型都表现出性能降低和偏向特定类别.

结论:

  • 当前的深度学习架构在sMRI痴呆症分类方面存在局限性,包括次优特征提取和类偏差.
  • 多模式注意力和3D-CNN模型提供更好的整体性能,但需要提高精度和概括性.
  • 未来的研究应该探索先进的计算机视觉技术和建筑修改,以提高诊断的准确性和效率.