Kalantar Motamedi Seyed Mohammad, Fatima Sahar, Zhang Qian, Foster Margaret J, Kolman Jacob M, Bageshwar Raaghav, Lilly James L, Lin Peter K, Lopez Adriana, Jones Stephen L, Lee Gyusung I, Sankaranarayanan Ganesh, Sasangohar Farzan, Stefanidis Dimitrios, Steadman Randolph H
Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, USA.
Department of Anesthesiology and Critical Care, Houston Methodist, 6565 Fannin St, B452, Houston, TX, 77030, USA.
J Robot Surg. 2025 Sep 4;19(1):554. doi: 10.1007/s11701-025-02689-4.
Defining performance errors in robotic surgery is critical for the assessment of robotic surgery skill. Our goal was to identify and categorize explicitly defined intraoperative technical errors in robotic surgery, how skill assessment was performed, and how ratings were conducted either manually by experts or via automated ratings. This scoping review included studies involving general, urologic, obstetrics/gynecologic, and thoracic surgery, and general skills as practiced in inanimate, virtual reality, in vivo/ex vivo animal, cadaver, and human operations. Primary empirical and consensus-building studies were included if they addressed intraoperative performance assessment or error definition and identification. MEDLINE (Ovid), Embase (Ovid), and Compendex were queried for results from 2012 to May 19, 2022. Of 2642 studies screened, 185 were included. The majority (n = 109, 60%) were US-based and involved either simulated surgical procedures using inanimate models (n = 88), virtual reality (n = 72), or intraoperative performance assessments of robotic surgeries in humans (n = 44); 36 studies combined two or more of these settings. Performance errors were explicitly defined in 104 articles (56%), and 64 used previously defined performance rating scales. The method of rating was split between manual (n = 137) and automated ratings (n = 85). Measures of performance vary considerably. More conceptual work is warranted to explicitly define errors that can inform robotic skill assessment. This is important given the growing interest in developing efficient and reliable objective measures of performance which are likely to rely on automated assessment methods.
定义机器人手术中的操作失误对于评估机器人手术技能至关重要。我们的目标是识别并明确分类机器人手术中明确界定的术中技术失误、技能评估的执行方式,以及专家手动评级或通过自动评级进行评级的方式。这项范围综述纳入了涉及普通外科、泌尿外科、妇产科和胸外科的研究,以及在无生命物体、虚拟现实、体内/体外动物、尸体和人体手术中所实践的一般技能研究。如果主要实证研究和建立共识的研究涉及术中性能评估或错误定义与识别,则纳入其中。检索了MEDLINE(Ovid)、Embase(Ovid)和Compendex,以获取2012年至2022年5月19日的结果。在筛选的2642项研究中,有185项被纳入。大多数研究(n = 109,60%)来自美国,涉及使用无生命模型的模拟手术程序(n = 88)、虚拟现实(n = 72)或人类机器人手术的术中性能评估(n = 44);36项研究结合了上述两种或更多种情况。104篇文章(56%)明确界定了操作失误,64篇使用了先前定义的性能评级量表。评级方法分为手动评级(n = 137)和自动评级(n = 85)。性能指标差异很大。需要开展更多概念性工作来明确界定可用于机器人技能评估的失误。鉴于人们越来越关注开发高效可靠的客观性能指标,而这可能依赖于自动评估方法,这一点很重要。