Department of Surgery, Carolinas Medical Center, 1000 Blythe Blvd, MEB Suite 601, Charlotte, NC, 28203, USA.
Division of HPB Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, NC, USA.
Surg Endosc. 2021 Jun;35(6):2765-2772. doi: 10.1007/s00464-020-07708-z. Epub 2020 Jun 16.
Current evaluation methods for robotic-assisted surgery (ARCS or GEARS) are limited to 5-point Likert scales which are inherently time-consuming and require a degree of subjective scoring. In this study, we demonstrate a method to break down complex robotic surgical procedures using a combination of an objective cumulative sum (CUSUM) analysis and kinematics data obtained from the da Vinci® Surgical System to evaluate the performance of novice robotic surgeons.
Two HPB fellows performed 40 robotic-assisted hepaticojejunostomy reconstructions to model a portion of a Whipple procedure. Kinematics data from the da Vinci® system was recorded using the dV Logger® while CUSUM analyses were performed for each procedural step. Each kinematic variable was modeled using machine learning to reflect the fellows' learning curves for each task. Statistically significant kinematics variables were then combined into a single formula to create the operative robotic index (ORI).
The inflection points of our overall CUSUM analysis showed improvement in technical performance beginning at trial 16. The derived ORI model showed a strong fit to our observed kinematics data (R = 0.796) with an ability to distinguish between novice and intermediate robotic performance with 89.3% overall accuracy.
In this study, we demonstrate a novel approach to objectively break down novice performance on the da Vinci® Surgical System. We identified kinematics variables associated with improved overall technical performance to create an objective ORI. This approach to robotic operative evaluation demonstrates a valuable method to break down complex surgical procedures in an objective, stepwise fashion. Continued research into objective methods of evaluation for robotic surgery will be invaluable for future training and clinical implementation of the robotic platform.
目前，机器人辅助手术（ARCS 或 GEARS）的评估方法仅限于 5 分制 Likert 量表，这种方法本身既耗时又需要一定程度的主观评分。在本研究中，我们展示了一种使用客观累积和（CUSUM）分析和从达芬奇®手术系统获得的运动学数据相结合的方法，用于评估新手机器人外科医生的手术表现。
两名肝胆胰外科医师进行了 40 例机器人辅助胆肠吻合术重建，以模拟 Whipple 手术的一部分。使用 dV Logger®记录达芬奇®系统的运动学数据，同时对每个手术步骤进行 CUSUM 分析。使用机器学习对每个运动学变量进行建模，以反映学员在每个任务中的学习曲线。然后将具有统计学意义的运动学变量组合成一个单一公式，创建手术机器人指数（ORI）。
我们的整体 CUSUM 分析的拐点表明，技术性能从第 16 次试验开始有所提高。衍生的 ORI 模型与我们观察到的运动学数据拟合良好（R=0.796），具有 89.3%的总体准确性，可以区分新手和中级机器人性能。
在本研究中，我们展示了一种新颖的方法，可以客观地分解达芬奇®手术系统新手的表现。我们确定了与整体技术性能提高相关的运动学变量，以创建客观的 ORI。这种机器人手术评估方法提供了一种有价值的方法，可以客观、逐步地分解复杂的手术程序。对机器人手术客观评估方法的进一步研究对于机器人平台的未来培训和临床应用将是非常宝贵的。
