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.

翻译：目的：本研究旨在探究外科住院医师在机器人手术技能方面的长期习得过程，以及训练间隔与疲劳对操作表现的影响。方法：在六个月内，外科住院医师每月参与三次训练课程，围绕一次26小时的医院轮班进行。每次轮班期间，他们分别参加轮班前、轮班中和轮班后安排的训练课程。在每次训练课程中，他们需完成三项干式实验室训练任务：环塔转移、打结与缝合。我们采集了包含视频（与运动学数据同步）、活动追踪数据以及缝合垫扫描图像在内的综合数据集。结果：我们收集了由18名不同外科专业的住院医师完成的972次试验数据集。参与者在所有任务中均表现出持续的性能提升。此外，我们发现不同指标和任务在轮班间隔期间的学习与遗忘存在差异，并观察到疲劳可能产生影响的迹象。结论：初步分析结果揭示了在较长训练间隔及不同疲劳水平下机器人手术技能的长期学习规律。意义：本研究为未来优化培训方案、增强人工智能在手术中的应用奠定了基础，最终有望提升患者治疗效果。该数据集将在期刊投稿录用后公开提供。