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相关概念视频

Endoscopic Studies I: Bronchoscopy and Thoracoscopy01:30

Endoscopic Studies I: Bronchoscopy and Thoracoscopy

Endoscopy is a non-surgical medical technique used to examine a person's internal organs and vessels. This lesson will focus on two types of endoscopic studies: bronchoscopy and thoracoscopy.
Bronchoscopy
Description
Bronchoscopy is a procedure that involves direct visualization of the larynx, trachea, and bronchi for diagnostic and therapeutic purposes. A flexible fiber optic or rigid bronchoscope is used to carry out the procedure. The fiber-optic bronchoscope is more frequently used due to...

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相关实验视频

Updated: May 12, 2026

Investigating the Three-dimensional Flow Separation Induced by a Model Vocal Fold Polyp
09:58

Investigating the Three-dimensional Flow Separation Induced by a Model Vocal Fold Polyp

Published on: February 3, 2014

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在视频内镜中使用深度学习方法来估计声姿势.

Francesca Pia Villani1, Maria Chiara Fiorentino2, Lorenzo Federici2

  • 1Department of Information Engineering, Universitá Politecnica delle Marche, Ancona, Italy. f.p.villani@staff.univpm.it.

Journal of imaging informatics in medicine
|February 12, 2025
PubMed
概括

这项研究引入了一个深度学习框架,用于准确的声 (VF) 从喉腔镜图像的姿势估计. 新的热图回归方法通过精确计算前喉角 (AGA) 来改善喉疾病的诊断.

关键词:
深度学习是一种深度学习.热图回归回归是一种热图回归.位置估计 位置估计视频内镜是指视频内镜.声乐折叠 声乐折叠 声乐折叠 声乐折叠

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Hemi-laryngeal Setup for Studying Vocal Fold Vibration in Three Dimensions
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Minimally Invasive Murine Laryngoscopy for Close-Up Imaging of Laryngeal Motion During Breathing and Swallowing
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相关实验视频

Last Updated: May 12, 2026

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 耳鼻喉科 耳鼻喉科 耳鼻喉科

背景情况:

  • 准确的声 (VF) 姿势估计对于诊断喉疾病和VF至关重要.
  • 目前的方法,如前角 (AGA) 估计是主观的,耗时的,需要专业知识.
  • 视频内镜检查是评估VF运动性的标准.

研究的目的:

  • 开发和验证一个深度学习框架,用于从临床喉科镜框架中精确的VF构成估计.
  • 改进客观和高效的评估VF运动性和相关参数,如AGA.
  • 克服现有方法的局限性,特别是在具有挑战性的临床场景中.

主要方法:

  • 开发了一个深度学习框架,利用热图回归在三个关键解剖点上.
  • 该框架估计了VF的位置,并从预测的关键点坐标计算了AGA.
  • 来自124名患者的471个喉腔镜的新数据集被用于评估.

主要成果:

  • 拟议的框架实现了VF姿势估计 (5.09像素) 的最低根平均平方误差 (RMSE).
  • 热图回归显示了AGA评估中最低的平均平均误差 (MAE).
  • 该方法在具有挑战性的图像中表现出卓越的表现,包括那些具有病理,噪音或闭塞的图像.

结论:

  • 关键点热图回归使精确的VF阵列估计成为可能,与最先进的方法相比,最大限度地减少错误.
  • 该框架提供了一种更客观,更有效的方法来评估VF运动性.
  • 这种深度学习解决方案提高了临床实践中喉疾病的诊断能力.