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

Alzheimer's Disease: Overview01:26

Alzheimer's Disease: Overview

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...
Alzheimer Disease l: Introduction01:29

Alzheimer Disease l: Introduction

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...

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

Deep Learning-Based Evaluation of Maxillary Dental Midline Deviation on Orthodontic Frontal Photographs.

Bioengineering (Basel, Switzerland)·2026
Same author

Biophysical network modeling of temporal and stereotyped sequence propagation of neural activity in the premotor nucleus HVC.

eLife·2025
Same author

SeruNet-MS: A Two-Stage Interpretable Framework for Multiple Sclerosis Risk Prediction with SHAP-Based Explainability.

Neurology international·2025
Same author

A Web-Deployed, Explainable AI System for Comprehensive Brain Tumor Diagnosis.

Neurology international·2025
Same author

Enhancing Melanoma Diagnosis with Advanced Deep Learning Models Focusing on Vision Transformer, Swin Transformer, and ConvNeXt.

Dermatopathology (Basel, Switzerland)·2024
Same author

Intrinsic neuronal properties represent song and error in zebra finch vocal learning.

Nature communications·2020

相关实验视频

Updated: Jun 7, 2026

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

40.8K

一个可解释的基于网络的阿尔茨海默病诊断系统,使用XRAI和深度学习在脑MRI上.

Serra Aksoy1, Arij Daou2,3

  • 1Institute of Computer Science, Ludwig Maximilian University of Munich (LMU), Oettingenstrasse 67, 80538 Munich, Germany.

Diagnostics (Basel, Switzerland)
|October 29, 2025
PubMed
概括

这项研究将可解释的AI (XAI) 与深度学习相结合,用于使用脑MRI扫描进行阿尔茨海默病 (AD) 严重程度分类. 开发的系统实现了高精度和效率,为早期AD检测提供了一个实用的工具.

关键词:
阿尔茨海默氏症是阿尔茨海默氏症的一种疾病.移动网络V3 移动网络V3一个XRAII的XRAI.这是分类分类的分类.深度学习是一种深度学习.可解释的人工智能 (XAI)基于网络的诊断接口.

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

8.4K
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

相关实验视频

Last Updated: Jun 7, 2026

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

40.8K
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

8.4K
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

科学领域:

  • 人工智能在医学中的应用
  • 神经成像分析分析 神经成像分析
  • 机器学习用于疾病分类.

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.
  • 目前用于AD的AI驱动的神经成像缺乏临床解释性和可用性.
  • 可解释性AI (XAI) 框架,如XRAI,可以通过可视化增强临床决策.

研究的目的:

  • 开发和评估一个临床部署的AI系统用于阿尔茨海默病的严重程度分类使用2D大脑MRI.
  • 整合XRAI以提高AI模型预测的可解释性.
  • 为实时AD诊断支持创建一个用户友好的Web界面.

主要方法:

  • 在一个增强的KaggleMRI数据集 (33,984张图像) 上训练了三个深度学习模型 (MobileNet-V3 Large,EfficientNet-B4,ResNet-50).
  • 在增强和原始数据集上评估模型性能,并将XRAI纳入基于区域的归因映射.
  • 开发了一个基于Gradio的网络接口,用于实时预测和视觉解释.

主要成果:

  • 移动Net-V3表现出高精度 (99.18%增强,99.47%原始) 和最小参数 (4.2M) 的卓越性能.
  • XRAI可视化与已知的AD神经解剖学模式相关,改善了临床解释性.
  • 网络接口提供了低于20秒的推断时间,具有高可靠性,支持临床工作流程.

结论:

  • 这项研究标志着XRAI首次系统地集成到基于MRI的深度学习AD严重程度分类中.
  • 移动Net-V3系统为临床使用提供了高准确度,高效率和可解释性的实用解决方案.
  • 这项研究为采用可解释的人工智能在早期和准确的阿尔茨海默病检测中提供了一条可行的途径.