Bahar Lidor, Sharon Yarden, Nisky Ilana
Department of Biomedical Engineering, Zlotowski Center of Neuroscience, Ben-Gurion University of the Negev, Be'er Sheva, Israel.
Front Neurorobot. 2020 Jan 24;13:108. doi: 10.3389/fnbot.2019.00108. eCollection 2019.
Robotic assisted minimally invasive surgery (RAMIS) systems present many advantages to the surgeon and patient over open and standard laparoscopic surgery. However, haptic feedback, which is crucial for the success of many surgical procedures, is still an open challenge in RAMIS. Understanding the way that haptic feedback affects performance and learning can be useful in the development of haptic feedback algorithms and teleoperation control systems. In this study, we examined the performance and learning of inexperienced participants under different haptic feedback conditions in a task of surgical needle driving via a soft homogeneous deformable object-an artificial tissue. We designed an experimental setup to characterize their movement trajectories and the forces that they applied on the artificial tissue. Participants first performed the task in an open condition, with a standard surgical needle holder, followed by teleoperation in one of three feedback conditions: (1) no haptic feedback, (2) haptic feedback based on position exchange, and (3) haptic feedback based on direct recording from a force sensor, and then again with the open needle holder. To quantify the effect of different force feedback conditions on the quality of needle driving, we developed novel metrics that assess the kinematics of needle driving and the tissue interaction forces, and we combined our novel metrics with classical metrics. We analyzed the final teleoperated performance in each condition, the improvement during teleoperation, and the aftereffect of teleoperation on the performance when using the open needle driver. We found that there is no significant difference in the final performance and in the aftereffect between the 3 conditions. Only the two conditions with force feedback presented statistically significant improvement during teleoperation in several of the metrics, but when we compared directly between the improvements in the three different feedback conditions none of the effects reached statistical significance. We discuss possible explanations for the relative similarity in performance. We conclude that we developed several new metrics for the quality of surgical needle driving, but even with these detailed metrics, the advantage of state of the art force feedback methods to tasks that require interaction with homogeneous soft tissue is questionable.
与开放式和标准腹腔镜手术相比，机器人辅助微创手术（RAMIS）系统为外科医生和患者带来了许多优势。然而，触觉反馈对于许多外科手术的成功至关重要，但在RAMIS中仍然是一个尚未解决的挑战。了解触觉反馈影响手术操作性能和学习的方式，对于触觉反馈算法和远程操作系统的开发可能会有所帮助。在本研究中，我们在通过柔软均匀的可变形物体——一种人工组织进行手术针驱动的任务中，研究了不同触觉反馈条件下无经验参与者的操作性能和学习情况。我们设计了一个实验装置来表征他们的运动轨迹以及他们施加在人工组织上的力。参与者首先在开放条件下使用标准手术持针器执行任务，然后在以下三种反馈条件之一进行远程操作：（1）无触觉反馈，（2）基于位置交换的触觉反馈，（3）基于力传感器直接记录的触觉反馈，之后再次使用开放持针器。为了量化不同力反馈条件对针驱动质量的影响，我们开发了新的指标来评估针驱动的运动学和组织相互作用力，并将我们的新指标与经典指标相结合。我们分析了每种条件下的最终远程操作性能、远程操作过程中的改进情况以及使用开放针驱动器时远程操作对性能的后效应。我们发现这三种条件下的最终性能和后效应没有显著差异。只有两种有力反馈的条件在远程操作过程中的几个指标上呈现出统计学上的显著改进，但当我们直接比较三种不同反馈条件下的改进情况时，没有一种效应达到统计学显著性。我们讨论了性能相对相似的可能解释。我们得出结论，我们为手术针驱动质量开发了几个新指标，但即使有这些详细指标，对于需要与均匀软组织相互作用的任务，现有最先进的力反馈方法的优势仍值得怀疑。
