Using hand pose estimation to automate open surgery training feedback.

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.