Surgeon Technical Skill Assessment using Computer Vision based Analysis
; Proceedings of the 2nd Machine Learning for Healthcare Conference, PMLR 68:88-99, 2017.
In this paper, we propose a computer vision based method to assess the technical skill level of surgeons by analyzing the movement of robotic instruments in robotic surgical videos. First, our method leverages the power of crowd workers on the internet to obtain high quality data in a scalable and cost-efficient way. Second, we utilize the high quality data to train an accurate and efficient robotic instrument tracker based on the state-of-the-art Hourglass Networks. Third, we assess the movement of the robotic instruments and automatically classify the technical level of a surgeon with a linear classifier, using peer evaluations of skill as the reference standard. Since the proposed method relies only on video data, this method has the potential to be transferred to other minimally invasive surgical procedures.