Jiang Bowen, Pennington Zach, Zhu Alex, Matsoukas Stavros, Ahmed A Karim, Ehresman Jeff, Mahapatra Smruti, Cottrill Ethan, Sheppell Hailey, Manbachi Amir, Crawford Neil, Theodore Nicholas
1Department of Neurosurgery, Johns Hopkins School of Medicine.
2Aristotle University of Thessaloniki School of Medicine, Thessaloniki, Greece; and.
J Neurosurg Spine. 2020 May 29;33(4):519-528. doi: 10.3171/2020.3.SPINE20208. Print 2020 Oct 1.
Robotic spine surgery systems are increasingly used in the US market. As this technology gains traction, however, it is necessary to identify mechanisms that assess its effectiveness and allow for its continued improvement. One such mechanism is the development of a new 3D grading system that can serve as the foundation for error-based learning in robot systems. Herein the authors attempted 1) to define a system of providing accuracy data along all three pedicle screw placement axes, that is, cephalocaudal, mediolateral, and screw long axes; and 2) to use the grading system to evaluate the mean accuracy of thoracolumbar pedicle screws placed using a single commercially available robotic system.
The authors retrospectively reviewed a prospectively maintained, IRB-approved database of patients at a single tertiary care center who had undergone instrumented fusion of the thoracic or lumbosacral spine using robotic assistance. Patients with preoperatively planned screw trajectories and postoperative CT studies were included in the final analysis. Screw accuracy was measured as the net deviation of the planned trajectory from the actual screw trajectory in the mediolateral, cephalocaudal, and screw long axes.
The authors identified 47 patients, 51% male, whose pedicles had been instrumented with a total of 254 screws (63 thoracic, 191 lumbosacral). The patients had a mean age of 61.1 years and a mean BMI of 30.0 kg/m2. The mean screw tip accuracies were 1.3 ± 1.3 mm, 1.2 ± 1.1 mm, and 2.6 ± 2.2 mm in the mediolateral, cephalocaudal, and screw long axes, respectively, for a net linear deviation of 3.6 ± 2.3 mm and net angular deviation of 3.6° ± 2.8°. According to the Gertzbein-Robbins grading system, 184 screws (72%) were classified as grade A and 70 screws (28%) as grade B. Placement of 100% of the screws was clinically acceptable.
The accuracy of the discussed robotic spine system is similar to that described for other surgical systems. Additionally, the authors outline a new method of grading screw placement accuracy that measures deviation in all three relevant axes. This grading system could provide the error signal necessary for unsupervised machine learning by robotic systems, which would in turn support continued improvement in instrumentation placement accuracy.
机器人脊柱手术系统在美国市场的应用日益广泛。然而，随着这项技术越来越受到关注，有必要确定评估其有效性并使其持续改进的机制。其中一种机制是开发一种新的三维分级系统，该系统可作为机器人系统中基于错误学习的基础。在本文中，作者试图：1）定义一个沿着椎弓根螺钉放置的所有三个轴（即头足轴、内外侧轴和螺钉长轴）提供准确性数据的系统；2）使用该分级系统评估使用单一商用机器人系统放置胸腰椎椎弓根螺钉的平均准确性。
作者回顾性分析了一家三级医疗中心前瞻性维护的、经机构审查委员会（IRB）批准的数据库，该数据库包含接受机器人辅助下胸段或腰骶段脊柱器械融合术的患者。最终分析纳入术前规划了螺钉轨迹且术后有CT检查的患者。螺钉准确性通过计划轨迹与实际螺钉轨迹在内外侧、头足和螺钉长轴上的净偏差来衡量。
作者确定了47例患者，其中男性占51%，其椎弓根共植入254枚螺钉（63枚胸段，191枚腰骶段）。患者的平均年龄为61.1岁，平均体重指数为30.0kg/m²。螺钉尖端在内外侧、头足和螺钉长轴上的平均准确性分别为1.3±1.3mm、1.2±1.1mm和2.6±2.2mm，净线性偏差为3.6±2.3mm，净角度偏差为3.6°±2.8°。根据格茨贝恩 - 罗宾斯分级系统，184枚螺钉（72%）被归类为A级，70枚螺钉（28%）为B级。所有螺钉的放置在临床上均可接受。
所讨论的机器人脊柱系统的准确性与其他手术系统相似。此外，作者概述了一种新的螺钉放置准确性分级方法，该方法可测量所有三个相关轴上的偏差。这种分级系统可以为机器人系统的无监督机器学习提供必要的错误信号，进而支持器械放置准确性的持续提高。
