  Purpose: This research aims to facilitate the use of state-of-the-art
computer vision algorithms for the automated training of surgeons and the
analysis of surgical footage. By estimating 2D hand poses, we model the
movement of the practitioner's hands, and their interaction with surgical
instruments, to study their potential benefit for surgical training.
  Methods: We leverage pre-trained models on a publicly-available hands dataset
to create our own in-house dataset of 100 open surgery simulation videos with
2D hand poses. We also assess the ability of pose estimations to segment
surgical videos into gestures and tool-usage segments and compare them to
kinematic sensors and I3D features. Furthermore, we introduce 6 novel surgical
dexterity proxies stemming from domain experts' training advice, all of which
our framework can automatically detect given raw video footage.
  Results: State-of-the-art gesture segmentation accuracy of 88.35\% on the
Open Surgery Simulation dataset is achieved with the fusion of 2D poses and I3D
features from multiple angles. The introduced surgical skill proxies presented
significant differences for novices compared to experts and produced actionable
feedback for improvement.
  Conclusion: This research demonstrates the benefit of pose estimations for
open surgery by analyzing their effectiveness in gesture segmentation and skill
assessment. Gesture segmentation using pose estimations achieved comparable
results to physical sensors while being remote and markerless. Surgical
dexterity proxies that rely on pose estimation proved they can be used to work
towards automated training feedback. We hope our findings encourage additional
collaboration on novel skill proxies to make surgical training more efficient.
