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，但需要执行复杂的运动任务，外科医生通常需要多年才能掌握。目前关于外科医生如何习得这些机器人手术技能的知识尚显不足。为弥补这一空白，先前一项研究追踪了外科住院医师在手术机器人上学习复杂手术干式实验室任务长达六个月的过程。错误是训练与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，错误难以实现自动监测。本文分析了环塔转移任务中的错误，该任务要求住院医师沿弯曲金属丝尽可能快速且精准地移动圆环。我们开发了一种图像处理算法，通过颜色与尺寸阈值、光流及短时傅里叶变换来检测碰撞错误，实现了约95%的检测准确率。利用检测到的错误与任务完成时间，我们发现住院医师在六个月内减少了任务完成时间与错误数量，而平均用于产生错误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错误相关反馈迈出重要一步。