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Purpose: This research aims to facilitate the use of state-of-the-artcomputer vision algorithms for the automated training of surgeons and theanalysis of surgical footage. By estimating 2D hand poses, we model themovement of the practitioner's hands, and their interaction with surgicalinstruments, to study their potential benefit for surgical training.  Methods: We leverage pre-trained models on a publicly-available hands datasetto create our own in-house dataset of 100 open surgery simulation videos with2D hand poses. We also assess the ability of pose estimations to segmentsurgical videos into gestures and tool-usage segments and compare them tokinematic sensors and I3D features. Furthermore, we introduce 6 novel surgicaldexterity proxies stemming from domain experts' training advice, all of whichour framework can automatically detect given raw video footage.  Results: State-of-the-art gesture segmentation accuracy of 88.35\% on theOpen Surgery Simulation dataset is achieved with the fusion of 2D poses and I3Dfeatures from multiple angles. The introduced surgical skill proxies presentedsignificant differences for novices compared to experts and produced actionablefeedback for improvement.  Conclusion: This research demonstrates the benefit of pose estimations foropen surgery by analyzing their effectiveness in gesture segmentation and skillassessment. Gesture segmentation using pose estimations achieved comparableresults to physical sensors while being remote and markerless. Surgicaldexterity proxies that rely on pose estimation proved they can be used to worktowards automated training feedback. We hope our findings encourage additionalcollaboration on novel skill proxies to make surgical training more efficient.

Using Hand Pose Estimation To Automate Open Surgery Training Feedback