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

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. To help bridge this gap, we previously followed surgical residents learning complex surgical training dry-lab tasks on a surgical robot over six months. Errors are an important measure for self-training and for skill evaluation, but unlike in virtual simulations, in dry-lab training, errors are difficult to monitor automatically. Here, we analyzed the 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 to detect collision errors and achieved detection accuracy of ~95%. Using the detected errors and task completion time, we found that the surgical residents decreased their completion time and number of errors over the six months. This analysis provides a framework for detecting collision errors in similar surgical training tasks and sheds light on the learning process of the surgical residents.

Dataset and Analysis of Long-Term Skill Acquisition in Robot-Assisted Minimally Invasive Surgery

Objective: We aim to investigate long-term robotic surgical skill acquisition among surgical residents and the effects of training intervals and fatigue on performance. Methods: For six months, surgical residents participated in three training sessions once a month, surrounding a single 26-hour hospital shift. In each shift, they participated in training sessions scheduled before, during, and after the shift. In each training session, they performed three dry-lab training tasks: Ring Tower Transfer, Knot-Tying, and Suturing. We collected a comprehensive dataset, including videos synchronized with kinematic data, activity tracking, and scans of the suturing pads. Results: We collected a dataset of 972 trials performed by 18 residents of different surgical specializations. Participants demonstrated consistent performance improvement across all tasks. In addition, we found variations in between-shift learning and forgetting across metrics and tasks, and hints for possible effects of fatigue. Conclusion: The findings from our first analysis shed light on the long-term learning processes of robotic surgical skills with extended intervals and varying levels of fatigue. Significance: This study lays the groundwork for future research aimed at optimizing training protocols and enhancing AI applications in surgery, ultimately contributing to improved patient outcomes. The dataset will be made available upon acceptance of our journal submission.