通过视觉算法自动检测机器人手术训练中的操作失误，揭示学习规律。
Video-Based Detection and Analysis of Errors in Robotic Surgical Training
- 用颜色、大小、光流和短时傅里叶变换分析视频，识别碰撞错误。
- 算法检测准确率达95%，6个月内学员完成时间与错误数均下降。
- 适合关注手术训练评估与智能反馈系统的研究者。
机器人辅助微创手术虽具优势，但需掌握复杂操作技能，医生需多年训练。当前对技能习得过程了解有限。一项前期研究追踪了外科住院医师在六个月内完成机器人手术干训任务的进展。由于真实训练中难以自动监测错误，本研究聚焦于环塔转移任务——即快速且精准地沿弯曲导线移动环体。我们开发了一种基于颜色与尺寸阈值、光流及短时傅里叶变换的图像处理算法，可自动检测碰撞错误，准确率约95%。结合检测到的错误与任务完成时间发现，六个月内学员完成时间与错误数量均减少，但出错时间占比平均保持稳定。该分析揭示了学习过程特征，为未来向机器人外科医生提供错误反馈奠定基础。
原文摘要 · Abstract (English)
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.
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