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

Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy01:26

Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy

108
This lesson explores three gastrointestinal imaging techniques: radionuclide testing, colonic transit studies, and virtual colonoscopy.
Radionuclide Testing
Radionuclide testing is a sophisticated medical technique for assessing gastrointestinal motility. It focuses on gastric emptying and colonic transit time. Radioactive markers track the movement of food through the digestive system, providing insights into gastrointestinal disorders.
In gastric emptying studies, a meal's liquid and...
108

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

Targeting the GLIS1/DNMT3B-mediated DNA Methylation of HOXA9 to Suppress Metastasis in Lung Adenocarcinoma.

Molecular and cellular biochemistry·2026
Same author

Robust 3D Semantic Occupancy Prediction With Calibration-Free Spatial Transformation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

ITGA1 promotes osteogenic differentiation of human periodontal ligament stem cells via FAK-mediated PI3K-Akt activation.

Stem cell research & therapy·2026
Same author

Experimental study of [<sup>68</sup>Ga]Ga-HER2-RGD, a dual-target molecular probe, for breast cancer PET/CT imaging.

EJNMMI research·2026
Same author

Comparison of [<sup>68</sup>Ga]Ga-FAPI PET/CT with CECT in the detection of peritoneal metastases from ovarian cancers and impact on clinical treatment decisions.

Abdominal radiology (New York)·2026
Same author

Multicenter fine annotated surgical video dataset for minimally invasive glaucoma surgery.

Scientific data·2026

相关实验视频

Updated: Jul 17, 2025

Minimally Invasive Murine Laryngoscopy for Close&#45;Up Imaging of Laryngeal Motion During Breathing and Swallowing
07:22

Minimally Invasive Murine Laryngoscopy for Close-Up Imaging of Laryngeal Motion During Breathing and Swallowing

Published on: December 1, 2023

555

时间微动作定位用于视频光学吞研究

Xianghui Ruan, Meng Dai, Zhuokun Chen

    IEEE journal of biomedical and health informatics
    |September 8, 2023
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了深度学习方法来分析视频光学吞研究 (VFSS),提高了吞时间参数评估的准确性. 这种新方法增强了VFSS中的微动作局部化,有助于诊断缺食症.

    更多相关视频

    Adapting Human Videofluoroscopic Swallow Study Methods to Detect and Characterize Dysphagia in Murine Disease Models
    08:32

    Adapting Human Videofluoroscopic Swallow Study Methods to Detect and Characterize Dysphagia in Murine Disease Models

    Published on: March 1, 2015

    21.3K
    Coordinate Mapping of Hyolaryngeal Mechanics in Swallowing
    14:13

    Coordinate Mapping of Hyolaryngeal Mechanics in Swallowing

    Published on: May 6, 2014

    18.3K

    相关实验视频

    Last Updated: Jul 17, 2025

    Minimally Invasive Murine Laryngoscopy for Close&#45;Up Imaging of Laryngeal Motion During Breathing and Swallowing
    07:22

    Minimally Invasive Murine Laryngoscopy for Close-Up Imaging of Laryngeal Motion During Breathing and Swallowing

    Published on: December 1, 2023

    555
    Adapting Human Videofluoroscopic Swallow Study Methods to Detect and Characterize Dysphagia in Murine Disease Models
    08:32

    Adapting Human Videofluoroscopic Swallow Study Methods to Detect and Characterize Dysphagia in Murine Disease Models

    Published on: March 1, 2015

    21.3K
    Coordinate Mapping of Hyolaryngeal Mechanics in Swallowing
    14:13

    Coordinate Mapping of Hyolaryngeal Mechanics in Swallowing

    Published on: May 6, 2014

    18.3K

    科学领域:

    • 医疗成像医学成像
    • 生物医学工程 生物医学工程
    • 人工智能的人工智能

    背景情况:

    • 视频光镜吞研究 (VFSS) 是对食障碍的检查的主要方法,依赖于时间参数进行评估.
    • 目前对VFSS数据的手动分析是耗时的,主观的,缺乏准确性.
    • 现有的方法难以应对VFSS微动作的独特挑战,包括小动作和可变持续时间.

    研究的目的:

    • 开发一种自动化方法,使用深度学习在VFSS中准确地提取时间参数.
    • 为了解决在VFSS数据中分析微动作所面临的挑战.
    • 为了提高失调评估的客观性和效率.

    主要方法:

    • 制定VFSS分析作为一个时间动作定位任务.
    • 开发和注释一个新的VFSS微动作数据集 (847项研究,71名受试者).
    • 引入粗到细的机制和可变大小的窗口生成器,以增强微动作本地化.

    主要成果:

    • 拟议的深度学习方法显著提高了VFSS中的微动作定位精度.
    • 与现有方法相比,性能从37.70%提高到46.10%.
    • 粗细的机制和可变大小的窗口发生器有效地处理了短,重复的微动作和可变的时间.

    结论:

    • 新的深度学习方法为分析VFSS数据提供了更准确和客观的方法.
    • 这一进步可以带来更好的诊断障碍的诊断能力.
    • 开发的数据集和方法作为未来吞分析研究的基准.