Deep Neural Skill Assessment And Transfer Application To Robotic Surgery Training

Due to the high sensitivity and complexity of robotic surgery tasks, acquiring appropriate skill levels by trainee surgeons through an effective training process is very important and affects the patient's safety and the quality of surgical outcomes. With the advanced deep learning technology and the recent availability of surgical procedures data, intelligent methods can be deployed to assess and transfer the skills of an experienced surgeon (mentor) to a novice surgeon (trainee). In this paper, we introduce a novel deep-learning-based skill transfer scheme consisting of a deep convolutional model, SkillNet, and a skill transfer algorithm for robotic surgery training. The proposed SkillNet extracts skill-related features of the mentor from different layers of the network. Then, trainee's maneuver is enhanced by the proposed skill transfer algorithm while minimizing deviations from the trainee's original intended trajectory. For validation, the JIGSAWS dataset and also our own experimental data were used to prove the generalizability of SkillNet in capturing skill-related features. The capability of the skill transfer algorithm in enhancing trainee trajectories in terms of predictability, hand tremor reduction, and noise cancellation were investigated separately. The obtained results indicate that this approach can be used as a high-performance filter that makes minor corrections to the input trajectory and improves the skill level of the trainee's trajectory in practice.