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在使用深度学习的结肠镜视频中基于密度聚类的自动解剖切片识别.

Byeong Soo Kim1, Minwoo Cho2,3,4, Goh Eun Chung5

  • 1Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, 08826, Korea.

Scientific reports
|January 9, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个深度学习系统,可以在结肠镜检查期间自动识别结肠部分. 人工智能模型准确地对解剖区域进行细分,有助于疾病诊断和自动报告.

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科学领域:

  • 医疗成像医学成像
  • 人工智能在医学中的应用
  • 胃肠病学 胃肠病学

背景情况:

  • 在结肠镜检查中准确识别解剖部分对于诊断结肠疾病和生成精确的报告至关重要.
  • 结肠的可变形性质对使用当前的深度学习方法进行可靠的解剖本地化提出了挑战.

研究的目的:

  • 开发和评估一种新的系统,用于在结肠镜检查过程中自动识别解剖切片,使用深度学习和密度聚类的组合.
  • 通过人工智能驱动的解剖细分来提高结肠镜报告的准确性和效率.

主要方法:

  • 使用100个结肠镜视频开发了一个系统,集成密度聚类和深度学习 (级联CNN模型).
  • 该系统连续估计尾孔 (AO),曲和"体外"地标.
  • 应用了DBSCAN算法用于解剖部分识别,整合临床知识和上下文,并进行预处理以删除非信息图像.

主要成果:

  • 该模型将结肠分为三个部分:右侧 (目眼,上升结肠),中间 (横向结肠) 和左侧 (下降结肠,直肠,直肠).
  • 解剖边界的估计出现时间误差为:AO 6.31s,肝曲 (HF) 9.79s,曲 (SF) 27.69s和"身体外" 3.26s.

结论:

  • 拟议的系统证明了基于人工智能的可行方法,用于在结肠镜检查中自动识别解剖切口.
  • 这种方法有可能在基于人工智能的自动结肠镜报告中节省时间,有效性和标准化.