Kassahun Yohannes, Yu Bingbin, Tibebu Abraham Temesgen, Stoyanov Danail, Giannarou Stamatia, Metzen Jan Hendrik, Vander Poorten Emmanuel
Robotics Innovation Center, German Research Center for Artificial Intelligence, Robert-Hooke-Str. 1, 28359, Bremen, Germany.
Faculty 3 - Mathematics and Computer Science, University of Bremen, Robert-Hooke-Str. 1, 28359, Bremen, Germany.
Int J Comput Assist Radiol Surg. 2016 Apr;11(4):553-68. doi: 10.1007/s11548-015-1305-z. Epub 2015 Oct 8.
Advances in technology and computing play an increasingly important role in the evolution of modern surgical techniques and paradigms. This article reviews the current role of machine learning (ML) techniques in the context of surgery with a focus on surgical robotics (SR). Also, we provide a perspective on the future possibilities for enhancing the effectiveness of procedures by integrating ML in the operating room.
The review is focused on ML techniques directly applied to surgery, surgical robotics, surgical training and assessment. The widespread use of ML methods in diagnosis and medical image computing is beyond the scope of the review. Searches were performed on PubMed and IEEE Explore using combinations of keywords: ML, surgery, robotics, surgical and medical robotics, skill learning, skill analysis and learning to perceive.
Studies making use of ML methods in the context of surgery are increasingly being reported. In particular, there is an increasing interest in using ML for developing tools to understand and model surgical skill and competence or to extract surgical workflow. Many researchers begin to integrate this understanding into the control of recent surgical robots and devices.
ML is an expanding field. It is popular as it allows efficient processing of vast amounts of data for interpreting and real-time decision making. Already widely used in imaging and diagnosis, it is believed that ML will also play an important role in surgery and interventional treatments. In particular, ML could become a game changer into the conception of cognitive surgical robots. Such robots endowed with cognitive skills would assist the surgical team also on a cognitive level, such as possibly lowering the mental load of the team. For example, ML could help extracting surgical skill, learned through demonstration by human experts, and could transfer this to robotic skills. Such intelligent surgical assistance would significantly surpass the state of the art in surgical robotics. Current devices possess no intelligence whatsoever and are merely advanced and expensive instruments.
技术和计算的进步在现代手术技术和模式的发展中发挥着越来越重要的作用。本文回顾了机器学习(ML)技术在手术领域的当前作用,重点关注手术机器人技术(SR)。此外,我们还展望了通过在手术室中整合ML来提高手术效果的未来可能性。
本综述聚焦于直接应用于手术、手术机器人技术、手术训练和评估的ML技术。ML方法在诊断和医学图像计算中的广泛应用不在本综述范围内。使用关键词组合在PubMed和IEEE Explore上进行搜索:ML、手术、机器人技术、外科和医疗机器人技术、技能学习、技能分析和感知学习。
越来越多的研究报告了在手术背景下使用ML方法的情况。特别是,人们越来越有兴趣使用ML来开发工具,以理解和建模手术技能与能力,或提取手术工作流程。许多研究人员开始将这种理解整合到对最新手术机器人和设备的控制中。
ML是一个不断扩展的领域。它很受欢迎,因为它允许高效处理大量数据以进行解释和实时决策。ML已经在成像和诊断中广泛使用,人们相信它也将在手术和介入治疗中发挥重要作用。特别是,ML可能会成为认知手术机器人概念的变革者。这种具备认知技能的机器人还将在认知层面上协助手术团队,例如可能降低团队的精神负担。例如,ML可以帮助提取通过人类专家示范学到的手术技能,并将其转化为机器人技能。这种智能手术辅助将显著超越手术机器人技术的现有水平。目前的设备没有任何智能,仅仅是先进且昂贵的器械。