评估净提取时间和结肠镜视频总结使用深度学习基于自动时间视频分割的视频总结
Kanggil Park1, Ji Young Lee2, Ahin Choi1
1Department of Biomedical Engineering, Asan Medical Center, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, 88, Olympic-Ro 43Gil, Songpa-Gu, Seoul, 05505, Republic of Korea.
一个新的深度学习模型通过排除非观察期来准确地测量结肠镜抽出时间. 这种人工智能工具提高了程序质量评估和聚检测率,以获得更好的患者结果.
科学领域:
- 胃肠病学 胃肠病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 适当的结肠镜抽取时间对于检测多重体至关重要,但传统测量通常是不准确的.
- 结肠镜检查期间的非观察性活动可能会影响禁用时间的评估,从而损害程序质量评估.
研究的目的:
- 开发一种深度学习 (DL) 模型,用于精确测量结肠镜检查中的净吸收时间.
- 创建一个DL模型,排除非观察阶段,并提供程序事件的定量视觉总结.
主要方法:
- 一个基于DL的自动时间视频细分模型被开发和训练在结肠镜视频.
- 该模型对关键事件进行了分类:盲目,干预,外部和窄带成像 (NBI) 模式.
- 计算了净提款时间,并提取了代表性图像用于视频总结.
主要成果:
- 在内部和外部测试中,DL模型在时间视频细分方面获得了超过93%的F1分数.
- 净停药时间与内镜师记录的时间有很强的相关性 (r > 0.97,p < 0.000).
- 生成的代表性图像准确地总结了关键的程序事件,由内镜评估证实.
结论:
- DL模型提供了一种高效,标准化和客观的方法来评估结肠镜程序质量.
- 这种人工智能工具有可能显著提高结肠镜中的临床实践和质量保证.
- 精确的净禁用时间测量可以提高多体检测率和患者护理.
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