Uncertainty-aware Self-supervised Learning for Cross-domain Technical Skill Assessment in Robot-assisted Surgery
This addresses the problem of automating skill assessment for surgical trainees in robot-assisted surgery, though it appears incremental as it builds on existing self-supervised learning techniques.
The paper tackles cross-domain technical skill assessment in robot-assisted surgery by proposing an uncertainty-aware self-supervised learning method that transfers knowledge from labeled to unlabeled kinematic data. The results show trainees with robotic assistance have significantly higher expert probability (p < 0.05) on a virtual reality training task.
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.