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相关实验视频

Updated: Jun 7, 2025

Application of Hemostatic Devices in Laparoscopic Hepatectomy
04:23

Application of Hemostatic Devices in Laparoscopic Hepatectomy

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基于深度学习的,在腹腔镜肝切除术中切除肝脏期间自动识别出血.

Taiki Sunakawa1,2,3, Daichi Kitaguchi3, Shin Kobayashi1

  • 1Department of Hepatobiliary and Pancreatic Surgery, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.

Surgical endoscopy
|November 18, 2024
PubMed
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研究人员开发了一种使用深度学习用于腹腔镜肝切除术 (LH) 的自动出血识别系统. 这种人工智能工具可以在肝脏手术期间识别出血区域,从而有可能提高患者的安全性和外科技能评估.

科学领域:

  • 手术创新 在外科创新.
  • 人工智能在医学中的应用
  • 医学成像分析 医学成像分析

背景情况:

  • 在腹腔镜肝切除术 (LH) 期间的手术内出血与不良的术后结果有关.
  • 有效的血液静止对于手术安全至关重要.
  • 目前在LH中检测出血的深度学习模型缺乏.

研究的目的:

  • 开发一种深度学习模型,用于在LH肝脏切割过程中自动识别出血区域.
  • 通过改进出血检测,提高腹腔镜肝切除术的安全性.

主要方法:

  • 使用LH视频进行回顾性可行性研究.
  • 血液区域的图像细分以每秒30的速度.
  • 开发一个卷积神经网络模型用于语义细分.
  • 使用精度,回忆和子系数进行评估.

主要成果:

  • 从44个LH视频中利用了2203张注释图像.
  • 模型的精度为0.76,回忆率为0.79,子系数为0.77.
  • 证明了深度学习的可行性,用于识别出血区域.

结论:

关键词:
在手术期间进行出血监测.腹腔镜外科手术的进展.机器学习算法 机器学习算法实时发现出血的检测.手术辅助系统手术辅助系统提高了外科手术的安全性

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Author Spotlight: Advancing Hepatobiliary and Pancreatic Tumor Treatment with Minimally Invasive Surgical Techniques
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  • 使用语义细分的自动出血识别模型成功开发和验证.
  • 该模型显示了实时手术内警报的潜力.
  • 未来的应用包括在LH手术中评估外科技巧.