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基于深度学习的手术步骤识别用于腹腔镜右侧大肠切除术.

Ryoya Honda^1,2, Daichi Kitaguchi³, Yuto Ishikawa¹

¹Department for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, Chiba, Japan.

Langenbeck's archives of surgery

|October 17, 2024

概括

这项研究开发了一种深度学习模型,在腹腔镜右侧切除术 (LAP-RC) 中自动识别手术步骤. 该模型实现了高精度,有助于标准化这一复杂的程序.

关键词:

卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.腹腔镜右侧大肠切除术是指右侧大肠切除术.阶段识别阶段识别实时自动识别实时自动识别标准化标准化标准化

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科学领域:

手术方面的创新.
医学成像分析分析医学成像分析
医学中的人工智能

背景情况:

镜右侧切除术 (LAP-RC) 涉及复杂的手术步骤,需要标准化.
深度学习 (DL) 为分析外科手术程序和改善标准化提供了潜力.

研究的目的:

开发基于DL的计算机视觉模型,用于在LAP-RC.中识别外科手术步骤.
评估开发的步骤识别模型的性能.

主要方法:

78个LAP-RC视频的回顾性分析 (腹腔镜腹腔镜切除和腹腔镜右侧半球切除).
视频被分为图像,并用于训练DL模型来分类八个或五个手术步骤.
使用精度,回忆,F1分数和整体准确度来评估性能.

主要成果:

对于八步分类,DL模型的整体准确度为72.1%;对于简化五步分类,总准确度为82.9%.
该研究包括35个LAP-ICR和44个LAP-RHC程序.

结论:

开发的DL模型证明了在LAP-RC期间识别外科手术步骤的有效性能.
自动化手术步骤识别可以有助于标准化LAP-RC程序.