Video-Based Detection and Analysis of Errors in Robotic Surgical Training

Abstract

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.