Search research articles

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 Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

相关概念视频

Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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

Blood-Brain Barrier Disruption Before Interhospital Transfer for Thrombectomy and Clinical Outcome.

Neurology·2026

Same author

International evidence-based recommendations for point-of-care lung ultrasound : 2025 focused update of the 2012 recommendations.

Intensive care medicine·2026

Same author

Thalamic nuclei insights into Alzheimer's disease.

bioRxiv : the preprint server for biology·2026

Same author

Core Blood-Brain Barrier Disruption in Patients With Large Vessel Occlusion.

Stroke·2026

Same author

Stepwise clinical and diagnostic strategy for coma of unknown origin.

Intensive care medicine·2026

Same author

Memory and Resting-State Connectivity in Acute Transient Global Amnesia: A Case-Control fMRI Study.

Annals of clinical and translational neurology·2026

Same journal

The authors reply.

Critical care medicine·2026

Same journal

Attracting Emergency Medicine Graduates to Surgical Critical Care Training Programs.

Critical care medicine·2026

Same journal

The authors reply.

Critical care medicine·2026

Same journal

Beyond a Snapshot: Tracking Family Prognostic Expectations in the ICU.

Critical care medicine·2026

Same journal

The authors reply.

Critical care medicine·2026

Same journal

Plasma Levels of Soluble ST2 Reflect Extrapulmonary Organ Dysfunction and Predict Outcomes in Acute Respiratory Failure: Beware of Potential Confounders.

Critical care medicine·2026

查看所有相关文章

Search research articles

相关实验视频

Updated: Feb 28, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

可解释的机器学习对昏迷的结果预测基于结构和功能大脑MRI.

Benjamine Sarton^1,2, Giulia Maria Mattia², Eve Cervoni^1,2

¹Critical Care Unit, University Teaching Hospital of Purpan, Toulouse, France.

Critical care medicine

|February 27, 2026

概括

此摘要是机器生成的。

分析高级MRI数据的机器学习模型可以准确预测昏迷诊断,脑损伤类型和患者康复. 这些模型确定了评估昏迷和预测结果的关键大脑网络指标.

关键词:

在昏迷中昏迷,昏迷中昏迷.可解释的人工智能机器学习是机器学习.一个介质电路.多式磁共振成像多式磁共振成像预后预后预后

更多相关视频

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

相关实验视频

Last Updated: Feb 28, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

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:

推先进的MRI用于昏迷评估,但识别相关指标是具有挑战性的.
现有的方法很难从昏迷患者的复杂MRI数据中提取有意义的信息.

研究的目的:

开发和验证可解释的机器学习 (ML) 管道,用于分析昏迷患者的高级MRI数据.
为了确定特定的MRI衍生的指标,表明昏迷状态,病因和神经恢复潜力.

主要方法:

一项前性横截面研究,涉及64名昏迷患者 (创伤或无氧) 和55名对照.
进行了先进的结构性MRI和休息状态功能连接性分析.
一组可解释的ML方法被应用并交叉验证用于分析.

主要成果:

ML模型在昏迷诊断 (93.4%),损伤歧视 (76.2%) 和结果预测 (82.4%) 中表现出高准确度.
50%的昏迷患者在3个月后经历了不利的神经结果.
这些模型在不同任务中显示出强大的概括能力.

结论:

一套新的脑部MRI衍生的指标有效地描述昏迷,其原因和恢复潜力.
中电路和前面对面网络的结构和功能完整性是关键指标.
这种ML方法为临床昏迷评估和预后提供了一个有前途的工具.