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

Multiple Sclerosis l: Introduction01:19

Multiple Sclerosis l: Introduction

20
Multiple sclerosis is a chronic autoimmune disease of the central nervous system (CNS) that affects the brain, spinal cord, and optic nerves. It is an inflammatory demyelinating disorder and a leading cause of neurological disability in young adults.EpidemiologyMS commonly begins between 20 and 40 years of age and is twice as common in women. Its exact cause remains unclear, but genetic susceptibility contributes, with higher risk in first-degree relatives and identical twins. A greater...
20

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

Efficacy and Safety of Intermittent Negative Pressure Treatment on Spasticity and Spasticity-Related Symptoms in Patients with Multiple Sclerosis: A Two-Arm Randomized Trial.

Neurology and therapy·2026
Same author

Multiple sclerosis and the limits of classical autoimmune theory.

Frontiers in immunology·2026
Same author

Rituximab versus Ocrelizumab in Newly Diagnosed Relapsing Multiple Sclerosis.

The New England journal of medicine·2026
Same author

Comparable Infection Risk of Ocrelizumab and Rituximab in Multiple Sclerosis in a Nationwide Swedish Cohort Study.

Annals of neurology·2026
Same author

Using EEG to Measure the Neural Effects of Oxytocin Administration: A Meta-Analysis and Systematic Review.

Psychophysiology·2026
Same author

Drivers of Rising Prevalence in Major Motor Neurodegenerative Diseases: Temporal Trends in Sweden and France (2003-2022).

Neurology·2026

相关实验视频

Updated: May 1, 2026

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

28.1K

多发性硬化症中的大脑年龄:使用深度学习和传统机器学习的研究.

Lars Skattebøl1,2, Gro O Nygaard1, Esten H Leonardsen3,4

  • 1Department of Neurology, Oslo University Hospital, Oslo 0450, Norway.

Brain communications
|May 8, 2025
PubMed
概括

在多发性硬化症中深度学习大脑年龄估计与时间学年龄的相关性比传统的机器学习更强. 这两种人工智能方法都将大脑年龄增加与残疾和疾病持续时间联系起来,深度学习可能会减少扫描器的变化.

关键词:
人工智能的人工智能是人工智能.大脑年龄差距的大脑年龄差距磁共振成像技术的使用神经退行症的神经退行症扫描仪的可变性 扫描仪可变性

更多相关视频

Comprehensive Autopsy Program for Individuals with Multiple Sclerosis
09:41

Comprehensive Autopsy Program for Individuals with Multiple Sclerosis

Published on: July 19, 2019

11.3K
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

907

相关实验视频

Last Updated: May 1, 2026

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

28.1K
Comprehensive Autopsy Program for Individuals with Multiple Sclerosis
09:41

Comprehensive Autopsy Program for Individuals with Multiple Sclerosis

Published on: July 19, 2019

11.3K
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

907

科学领域:

  • 神经成像是一种神经成像.
  • 人工智能的人工智能
  • 神经退行性疾病 神经退行性疾病

背景情况:

  • 多发性硬化症 (MS) 中加速的大脑衰老与残疾相关.
  • 人工智能 (AI) 提供了用于量化神经退行症的工具.
  • 对MS中大脑年龄的传统机器学习 (ML) 与深度学习 (DL) 的比较数据缺乏.

研究的目的:

  • 在多发性硬化症 (MS) 中验证深度学习 (DL) 大脑年龄模型.
  • 为了比较DL和传统ML模型来估计MS中的大脑年龄.
  • 评估大脑年龄与临床结果和扫描器变异性的关联.

主要方法:

  • 对1516名多发性硬化症患者的4584张MRI扫描进行了回顾性分析.
  • 简单的DL完全卷积网络与传统的ML模型用于大脑年龄估计的比较.
  • 从纵向队列中分析临床和MRI数据,使用统一的后处理管道.

主要成果:

  • DL大脑年龄估计与时间年龄 (r=0.90) 的相关性比传统的ML (r=0.75) 更强.
  • 通过这两种方法估计的大脑年龄增加与残疾 (EDSS分数) 和疾病持续时间显著相关.
  • 与传统的ML相比,DL模型显示扫描器变异性较小.

结论:

  • 深度学习衍生的大脑年龄是MS的有效衡量标准,与临床残疾密切相关.
  • DL大脑年龄估计的性能与传统的ML相提并论,但可能会改善对扫描器差异的稳定性.
  • 人工智能驱动的大脑年龄评估对监测MS中神经退行有希望.