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

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

Survival estimates and their predictors in genetic frontotemporal dementia: an international, retrospective, cohort study.

The Lancet. Neurology·2026
Same author

Elevated sclerostin levels in cerebrospinal fluid are associated with cognitive impairment in the Alzheimer's disease continuum.

Alzheimer's & dementia (Amsterdam, Netherlands)·2026
Same author

Neuropsychological, biological, and electrophysiological outcomes of gamma-tACS in MCI-AD: A case series.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology·2026
Same author

Trajectories of brain structure and function in young adult carriers of genetic frontotemporal dementia variants.

medRxiv : the preprint server for health sciences·2026
Same author

Incidence of Dementia With Lewy Bodies in Salento, Italy: A Population-Based Study.

Neurology·2026
Same author

Unraveling sleep dysfunction in progressive supranuclear palsy: a pilot study on the role of periodic limb movements in sleep and insomnia.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology·2026

相关实验视频

Updated: Jun 29, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

可解释的机器学习放射学模型用于初级渐进性失言症分类.

Benedetta Tafuri1,2, Roberto De Blasi2, Salvatore Nigro2

  • 1Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari Aldo Moro, Bari, Italy.

Frontiers in systems neuroscience
|April 2, 2024
PubMed
概括

这项研究表明,对脑部扫描的放射学分析可以准确地区分一种语言障碍,即初级渐进性失言症 (PPA) 的亚型. 机器学习确定了用于诊断语义 (svPPA) 和非流利 (nfvPPA) 变体的关键白质特征.

关键词:
这就是为什么MRI是MRI.主要的渐进性失言症.可以解释性的解释性.机器学习 (ML) 是指机器学习.无线电学 (radiomics) 是一种无线电学.

更多相关视频

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K
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

相关实验视频

Last Updated: Jun 29, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K
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

科学领域:

  • 神经成像是一种神经成像.
  • 机器学习 机器学习
  • 神经学 神经学

背景情况:

  • 初级渐进性失言症 (PPA) 是一种神经退行性疾病,导致语言障碍.
  • 关键的子类型包括语义 (svPPA) 和非流利/语法 (nfvPPA) 变体.
  • 准确的诊断需要整合临床,生物和放射学数据.

研究的目的:

  • 评估白质组织分析的实用性,使用放射学来对PPA进行分类.
  • 采用可解释的机器学习来识别差异诊断的关键特征.
  • 为了提高svPPA和nfvPPA的诊断准确度.

主要方法:

  • 在56名PPA患者和53名对照患者的T1加权MRI扫描上进行白质质纹理分析.
  • 训练基于树的算法,结合临床和放射学测量.
  • 使用Shapley添加式解释 (SHAP) 进行特征重要性分析.

主要成果:

  • 放射学模型实现了95%的准确性,将svPPA与对照区分开来,93.7%的准确性将svPPA与nfvPPA区分开来.
  • nfvPPA患者与对照患者的区别是93.7%的准确度.
  • SHAP分析突出显示左侧内腔皮层附近的白质对于分类至关重要.

结论:

  • 放射性特征对于分类svPPA和nfvPPA亚型非常有价值.
  • 可解释的人工智能有效地识别了PPA的关键诊断特征.
  • 这种方法提高了PPA的差异诊断.