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相关概念视频

Anatomical Positions01:11

Anatomical Positions

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In anatomy, several standard anatomical positions are used as references for describing the position and orientation of different body parts. These positions help provide a common frame of reference when discussing anatomical structures. The anatomical position is the standard reference point for describing the body's position and orientation. In this position:
The body is upright, facing forward, and standing erect.
The feet are parallel and flat on the floor.
The arms are hanging by the...
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相关实验视频

Updated: Jun 27, 2025

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans
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基于深度学习的解剖位置识别用于胃镜检查.

Xiufeng Su1, Weiyu Liu1, Suyi Jiang1

  • 1Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, Shandong, China.

Technology and health care : official journal of the European Society for Engineering and Medicine
|April 26, 2024
PubMed
概括

这项研究引入了一个深度学习模型,用于在胃镜图像中自动识别解剖位置. 开发的MogaNet模型显著提高了准确性,帮助初级医生进行完整的胃镜检查.

关键词:
胃镜图像 胃镜图像解剖位置识别 解剖位置识别卷积神经网络是一种卷积神经网络.

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科学领域:

  • 医学成像分析分析 医学成像分析
  • 医疗保健中的人工智能
  • 胃肠病学 胃肠病学

背景情况:

  • 胃镜检查对于检测上部胃肠病变至关重要.
  • 目前的胃镜培训面临挑战,因为初级医生严格的图像存档要求.
  • 准确的解剖位置识别对于全面的检查和记录至关重要.

研究的目的:

  • 开发一种基于深度学习的自动化系统,用于在胃镜检查期间识别解剖位置.
  • 提高医疗专业人员在内镜方面的培训和诊断能力.

主要方法:

  • 使用了包括8个解剖学类别的17,182张胃镜图像的数据集.
  • 使用MogaNet卷积神经网络模型进行解剖位置识别.
  • 使用灵敏度,精度和F1得分指标严格评估模型性能.

主要成果:

  • 与ResNet,GoogleNet和SqueezeNet.Net相比,提出的MogaNet模型取得了更高的性能.
  • 莫加网模型的平均灵敏度,精度和F1得分分别为0.963,0.964和0.964.
  • 统计分析证实了与现有模型相比的显著改善 (p < 0.05).

结论:

  • 开发的深度学习方法在胃镜检查期间识别解剖位置方面表现出色.
  • 这项技术可以帮助初级医生有效地满足检查完整性和图像存档标准.
  • 自动位置识别有望提高内镜培训和实践的质量和一致性.