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

Repeat Ipsilateral Focused Ultrasound Thalamotomy after Tremor Recurrence: Safety, Efficacy, and Lesion Analysis.

Movement disorders : official journal of the Movement Disorder Society·2026
Same author

Neurocognitive and affective dysfunction in Chiari malformation type I.

Journal of neurosurgery. Pediatrics·2026
Same author

Developmental genetic determinants of the human cerebrospinal fluid-ventricular system.

Science translational medicine·2026
Same author

Treatment Effect Reanalysis of the Randomized Individual Screening Trial of Innovative Glioblastoma Therapy in Newly Diagnosed Glioblastoma With External Control Data.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026
Same author

Patient-friendly simplification and translation of neuroradiology impressions using artificial intelligence.

Scientific reports·2026
Same author

De Novo <i>TRIO</i> Missense Variants Disrupt Ras-GEF Domains and Cause Congenital Ventriculomegaly and Hydrocephalus.

Human mutation·2026

相关实验视频

Updated: Jun 11, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

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

Published on: April 13, 2013

42.5K

采用卷积神经网络的自动心室细分和分流故障检测.

Kevin T Huang1,2, Jack McNulty3,4,5, Helweh Hussein4

  • 1Harvard Medical School, 25 Shattuck St, Boston, MA, 02115, USA. khuang@bwh.harvard.edu.

Scientific reports
|September 28, 2024
PubMed
概括

计算机视觉算法可以准确地检测腹腔大脑病,这是成年人脑水性疾病的标志. 这项技术在预测变送器修改的需要方面显示出高可靠性,改善了诊断.

关键词:
成年人的水脑.计算机视觉 计算机视觉 计算机视觉机器学习是机器学习.腹腔室的分离方式

更多相关视频

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

376
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K

相关实验视频

Last Updated: Jun 11, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

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

Published on: April 13, 2013

42.5K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

376
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K

科学领域:

  • 神经外科 神经外科
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 成年人脑水性主要用心室静脉转移来治疗.
  • 短路器的故障是常见的并发症,带来了诊断挑战.

研究的目的:

  • 评估计算机视觉算法的可行性,以自动检测成年水脑病患者的腹腔大.
  • 为了评估算法的准确性预测分流失败.

主要方法:

  • 八年来对191名成人脑水性病患者的CT扫描进行了回顾性分析.
  • 训练一个机器学习算法来识别心室和检测心室隆骨病.
  • 将算法性能与使用子得分和心室体积计算的人类审查员进行比较.

主要成果:

  • 该算法实现了0.809 ± 0.094.09的平均子得分.
  • 计算机衍生的心室体积与人类评估没有显著差异.
  • 在所有测试案例中,该算法正确地识别了心室积血病,并以92.3%的准确度预测了变频修订需求.

结论:

  • 自动化算法可以可靠且准确地检测成年水脑位功能故障的腹腔巨.
  • 这项技术提供了一个可行的解决方案,以改善分流器故障的诊断.