Robot-assisted minimally invasive surgeries offer many advantages but require complex motor tasks that take surgeons years to master. There is currently a lack of knowledge on how surgeons acquire these robotic surgical skills. Toward bridging this gap, a previous study followed surgical residents learning complex surgical dry lab tasks on a surgical robot over six months. Errors are an important measure for training and skill evaluation, but unlike in virtual simulations, in dry lab training, errors are difficult to monitor automatically. Here, we analyzed errors in the ring tower transfer task, in which surgical residents moved a ring along a curved wire as quickly and accurately as possible. We developed an image-processing algorithm using color and size thresholds, optical flow and short time Fourier transforms to detect collision errors and achieved a detection accuracy of approximately 95%. Using the detected errors and task completion time, we found that the residents reduced their completion time and number of errors over the six months, while the percentage of task time spent making errors remained relatively constant on average. This analysis sheds light on the learning process of the residents and can serve as a step towards providing error-related feedback to robotic surgeons.

翻译：机器人辅助微创手术具有诸多优势，但需要外科医生掌握复杂的运动技能，这通常需要数年时间才能精通。目前关于外科医生如何获得这些机器人手术技能的知识尚不充分。为弥补这一空白，先前一项研究追踪了外科住院医师在六个月内通过手术机器人学习复杂手术干式实验室任务的过程。误差是培训和技能评估的重要指标，但与虚拟仿真不同，在干式实验室训练中误差难以自动监测。本文分析了环塔转移任务中的误差，该任务要求住院医师尽可能快速准确地将圆环沿弯曲金属丝移动。我们开发了一种图像处理算法，利用颜色与尺寸阈值、光流和短时傅里叶变换来检测碰撞误差，实现了约95%的检测准确率。通过检测到的误差和任务完成时间，我们发现住院医师在六个月内减少了任务完成时间和误差数量，而平均用于产生误差的任务时间占比保持相对稳定。该分析揭示了住院医师的学习过程，可为向机器人外科医生提供误差相关反馈迈出重要一步。