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Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions

机译:超越增强的灵活性仪器的手术机器人技术:机器学习技术及其在智能和自主手术动作中的作用的调查

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摘要

AbstractPurpose: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.Methods: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.Results: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.Conclusion: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.
机译:摘要目的:技术和计算的进步在现代外科技术和范例的发展中起着越来越重要的作用。本文回顾了机器学习(ML)技术在外科手术中的当前作用,重点是外科手术机器人(SR)。此外,我们还提供了通过将ML集成到手术室中来提高程序有效性的未来可能性的方法。方法:本文的重点是ML技术直接应用于手术,外科机器人技术,手术培训和评估。机器学习方法在诊断和医学图像计算中的广泛使用超出了本文的范围。搜索是在PubMed和IEEE Explore上使用关键词组合进行的:ML,手术,机器人,外科和医疗机器人,技能学习,技能分析和感知学习。结果:在外科领域中使用ML方法的研究越来越多报告。特别地,使用ML开发工具来理解和建模手术技能和能力或提取手术流程的兴趣日益浓厚。许多研究人员开始将这种理解整合到最近的外科手术机器人和设备的控制中。结论:机器语言是一个不断扩展的领域。它之所以受欢迎,是因为它可以有效地处理大量数据,以进行解释和实时决策。人们已经认为ML在外科手术和介入治疗中也将在成像和诊断中广泛使用。特别是,ML可能会改变认知外科手术机器人的观念。这种具有认知技能的机器人还将在认知水平上帮助外科手术团队,例如可能降低团队的精神负担。例如,机器学习可以帮助提取外​​科技能,这是通过人类专家的示范中学到的,并且可以将其转化为机器人技能。这样的智能外科手术辅助将大大超越外科手术机器人领域的技术水平。当前的设备没有任何智能,仅仅是先进且昂贵的仪器。

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