Padoy Nicolas
a ICube, IHU Strasbourg, CNRS , University of Strasbourg , Strasbourg , France.
Minim Invasive Ther Allied Technol. 2019 Apr;28(2):82-90. doi: 10.1080/13645706.2019.1584116. Epub 2019 Mar 8.
Recent years have seen tremendous progress in artificial intelligence (AI), such as with the automatic and real-time recognition of objects and activities in videos in the field of computer vision. Due to its increasing digitalization, the operating room (OR) promises to directly benefit from this progress in the form of new assistance tools that can enhance the abilities and performance of surgical teams. Key for such tools is the recognition of the surgical workflow, because efficient assistance by an AI system requires this system to be aware of the surgical context, namely of all activities taking place inside the operating room. We present here how several recent techniques relying on machine and deep learning can be used to analyze the activities taking place during surgery, using videos captured from either endoscopic or ceiling-mounted cameras. We also present two potential clinical applications that we are developing at the University of Strasbourg with our clinical partners.
近年来，人工智能（AI）取得了巨大进展，例如在计算机视觉领域中对视频中的物体和活动进行自动实时识别。由于手术室（OR）的数字化程度不断提高，有望直接受益于这一进展，出现新的辅助工具，从而提升手术团队的能力和表现。此类工具的关键在于识别手术流程，因为人工智能系统的有效辅助需要该系统了解手术背景，即手术室内部发生的所有活动。我们在此展示了如何利用一些基于机器学习和深度学习的最新技术，通过对内窥镜或天花板安装摄像头拍摄的视频进行分析，来分析手术过程中发生的活动。我们还展示了斯特拉斯堡大学与临床合作伙伴正在开发的两项潜在临床应用。
