  Open, or non-laparoscopic surgery, represents the vast majority of all
operating room procedures, but few tools exist to objectively evaluate these
techniques at scale. Current efforts involve human expert-based visual
assessment. We leverage advances in computer vision to introduce an automated
approach to video analysis of surgical execution. A state-of-the-art
convolutional neural network architecture for object detection was used to
detect operating hands in open surgery videos. Automated assessment was
expanded by combining model predictions with a fast object tracker to enable
surgeon-specific hand tracking. To train our model, we used publicly available
videos of open surgery from YouTube and annotated these with spatial bounding
boxes of operating hands. Our model's spatial detections of operating hands
significantly outperforms the detections achieved using pre-existing
hand-detection datasets, and allow for insights into intra-operative movement
patterns and economy of motion.
