Objective technical skill assessment is crucial for effective training of new surgeons in robot-assisted surgery. With advancements in surgical training programs in both physical and virtual environments, it is imperative to develop generalizable methods for automatically assessing skills. In this paper, we propose a novel approach for skill assessment by transferring domain knowledge from labeled kinematic data to unlabeled data. Our approach leverages labeled data from common surgical training tasks such as Suturing, Needle Passing, and Knot Tying to jointly train a model with both labeled and unlabeled data. Pseudo labels are generated for the unlabeled data through an iterative manner that incorporates uncertainty estimation to ensure accurate labeling. We evaluate our method on a virtual reality simulated training task (Ring Transfer) using data from the da Vinci Research Kit (dVRK). The results show that trainees with robotic assistance have significantly higher expert probability compared to these without any assistance, p < 0.05, which aligns with previous studies showing the benefits of robotic assistance in improving training proficiency. Our method offers a significant advantage over other existing works as it does not require manual labeling or prior knowledge of the surgical training task for robot-assisted surgery.

翻译：客观的技术技能评估对机器人辅助手术中新型外科医生的有效培训至关重要。随着物理与虚拟环境中手术培训项目的进步，开发可泛化的自动化技能评估方法势在必行。本文提出一种通过将标注运动学数据的领域知识迁移至未标注数据的新型技能评估方法。该方法利用常见手术训练任务（如缝合、穿针及打结）的标注数据，联合训练一个同时处理标注与未标注数据的模型。通过迭代方式为未标注数据生成伪标签，并融入不确定性估计以保证标注准确性。我们使用达芬奇研究套件（dVRK）数据在虚拟现实模拟训练任务（环转移）上评估方法性能。结果表明，与无辅助组相比，机器人辅助受训者的专家概率显著更高（p < 0.05），这与先前关于机器人辅助提升训练效能的研究结论一致。相较现有方法，本方法优势显著：无需手动标注或机器人辅助手术训练任务的先验知识。