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术中操作情境识别系统的最新技术。

State-of-the-art of situation recognition systems for intraoperative procedures.

机构信息

School of Informatics, Research Group Computer Assisted Medicine (CaMed), Reutlingen University, Alteburgstr. 150, 72762, Reutlingen, Germany.

出版信息

Med Biol Eng Comput. 2022 Apr;60(4):921-939. doi: 10.1007/s11517-022-02520-4. Epub 2022 Feb 17.

Abstract

One of the key challenges for automatic assistance is the support of actors in the operating room depending on the status of the procedure. Therefore, context information collected in the operating room is used to gain knowledge about the current situation. In literature, solutions already exist for specific use cases, but it is doubtful to what extent these approaches can be transferred to other conditions. We conducted a comprehensive literature research on existing situation recognition systems for the intraoperative area, covering 274 articles and 95 cross-references published between 2010 and 2019. We contrasted and compared 58 identified approaches based on defined aspects such as used sensor data or application area. In addition, we discussed applicability and transferability. Most of the papers focus on video data for recognizing situations within laparoscopic and cataract surgeries. Not all of the approaches can be used online for real-time recognition. Using different methods, good results with recognition accuracies above 90% could be achieved. Overall, transferability is less addressed. The applicability of approaches to other circumstances seems to be possible to a limited extent. Future research should place a stronger focus on adaptability. The literature review shows differences within existing approaches for situation recognition and outlines research trends. Applicability and transferability to other conditions are less addressed in current work.

摘要

自动辅助的一个关键挑战是根据手术过程的状态为手术室中的操作人员提供支持。因此,使用在手术室中收集的上下文信息来获取有关当前情况的知识。在文献中,已经存在针对特定用例的解决方案,但这些方法在多大程度上可以转移到其他条件下还存在疑问。我们对术中区域现有的情况识别系统进行了全面的文献研究,涵盖了 2010 年至 2019 年间发表的 274 篇文章和 95 篇交叉引用。我们根据使用的传感器数据或应用领域等定义的方面对 58 种已确定的方法进行了对比和比较。此外,我们还讨论了适用性和可转移性。大多数论文都侧重于用于识别腹腔镜和白内障手术中情况的视频数据。并非所有方法都可以在线实时识别。使用不同的方法,可以实现识别准确率超过 90%的良好效果。总体而言,可转移性的关注度较低。方法对其他情况的适用性似乎在一定程度上是可能的。未来的研究应更加注重适应性。文献综述显示了现有情况识别方法之间的差异,并概述了研究趋势。在当前的工作中,对其他条件下的适用性和可转移性的关注较少。

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State-of-the-art of situation recognition systems for intraoperative procedures.术中操作情境识别系统的最新技术。
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本文引用的文献

3
Surgical data processing for smart intraoperative assistance systems.用于智能术中辅助系统的手术数据处理
Innov Surg Sci. 2017 Sep 9;2(3):145-152. doi: 10.1515/iss-2017-0035. eCollection 2017 Sep.
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Novel evaluation of surgical activity recognition models using task-based efficiency metrics.基于任务效率指标的手术活动识别模型的新评估。
Int J Comput Assist Radiol Surg. 2019 Dec;14(12):2155-2163. doi: 10.1007/s11548-019-02025-w. Epub 2019 Jul 2.
8
Machine and deep learning for workflow recognition during surgery.用于手术过程中工作流程识别的机器与深度学习
Minim Invasive Ther Allied Technol. 2019 Apr;28(2):82-90. doi: 10.1080/13645706.2019.1584116. Epub 2019 Mar 8.
10
"Deep-Onto" network for surgical workflow and context recognition.“Deep-Onto”网络用于手术流程和上下文识别。
Int J Comput Assist Radiol Surg. 2019 Apr;14(4):685-696. doi: 10.1007/s11548-018-1882-8. Epub 2018 Nov 16.

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