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

Surgical Video Understanding with Alignment-Preserving Temporal Adaptation and Action Triplet Text Alignment.

Bioengineering (Basel, Switzerland)·2026
Same author

Privacy-Aware Continual Self-Supervised Learning on Multi-Window Chest Computed Tomography for Domain-Shift Robustness.

Bioengineering (Basel, Switzerland)·2026
Same author

Feasibility and Acceptability of a Deep-Learning-Based Nipple Trauma Assessment System for Postpartum Breastfeeding Support.

Healthcare (Basel, Switzerland)·2025
Same author

Clinical evaluation of motion robust reconstruction using deep learning in lung CT.

Physical and engineering sciences in medicine·2025
Same author

The comparison of MRI and CT protocol examination times for mechanical thrombectomy in acute ischemic stroke.

Radiological physics and technology·2025
Same author

Role of Silver Nipple Protectors in Treating Nipple Trauma: A Non-Randomized Comparative Trial.

Journal of human lactation : official journal of International Lactation Consultant Association·2025

相关实验视频

Updated: Jul 23, 2025

Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia
07:25

Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia

Published on: September 22, 2020

3.5K

通过ADC值分析预测机械血栓切除术结果和时间限制:使用机器学习进行全面的临床和模拟研究.

Daisuke Oura1,2, Soichiro Takamiya3, Riku Ihara1

  • 1Department of Radiology, Otaru General Hospital, Otaru 047-0152, Japan.

Diagnostics (Basel, Switzerland)
|July 14, 2023
PubMed
概括

机器学习准确地预测了接受机械血栓切除术 (MT) 的急性缺血性中风 (AIS) 患者的结果. 详细的扩散成像分析有助于模拟MT的安全时间限制,改善患者护理.

关键词:
这是一个ADCADC ADC.这就是为什么MRI是MRI.急性缺血性中风是急性缺血性中风.机器学习是机器学习.

更多相关视频

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

8.3K
Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
09:52

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Published on: January 15, 2017

17.2K

相关实验视频

Last Updated: Jul 23, 2025

Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia
07:25

Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia

Published on: September 22, 2020

3.5K
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

8.3K
Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
09:52

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Published on: January 15, 2017

17.2K

科学领域:

  • 神经学 神经学
  • 放射学 放射学是一门学科.
  • 生物医学工程 生物医学工程

背景情况:

  • 在机械血栓切除术 (MT) 后,预测急性缺血性中风 (AIS) 患者的结果是复杂的.
  • 表面扩散系数 (ADC) 分析为组织活力提供了潜在的见解.

研究的目的:

  • 评估机器学习 (ML) 使用详细的ADC分析来预测MT后患者的结果.
  • 在基于ADC参数的AIS中模拟MT的时间限制.

主要方法:

  • 利用具有不同值的ADC分析来提取定量成像特征.
  • 在75名AIS患者中使用了额外树分类模型,完成了再输血.
  • 模拟的MT时间限制使用预测得分和反时间.

主要成果:

  • 额外树分类器在预测结果方面实现了高准确度 (93.3%) 和AUC (0.833).
  • 模拟数据显示,随着更长的再输血时间,预测得分下降.
  • 年轻的患者表现出MT的模拟时间限制较长.

结论:

  • ML与详细的ADC分析相结合,对于预测AIS患者的MT后结果是有效的.
  • 这种方法可以模拟MT的时间耐受性,帮助临床决策.