Automatic alignment of surgical videos using kinematic data
This addresses the challenge of surgical education by enabling better learning from video comparisons, though it is incremental as it builds on existing alignment methods.
The paper tackles the problem of high inter-operator variability in surgical gesture duration and execution, which makes comparing novice to expert surgical videos difficult, by proposing a novel technique to align multiple videos using kinematic data and Dynamic Time Warping, resulting in synchronized videos that show the same gesture performed at different speeds.
Over the past one hundred years, the classic teaching methodology of "see one, do one, teach one" has governed the surgical education systems worldwide. With the advent of Operation Room 2.0, recording video, kinematic and many other types of data during the surgery became an easy task, thus allowing artificial intelligence systems to be deployed and used in surgical and medical practice. Recently, surgical videos has been shown to provide a structure for peer coaching enabling novice trainees to learn from experienced surgeons by replaying those videos. However, the high inter-operator variability in surgical gesture duration and execution renders learning from comparing novice to expert surgical videos a very difficult task. In this paper, we propose a novel technique to align multiple videos based on the alignment of their corresponding kinematic multivariate time series data. By leveraging the Dynamic Time Warping measure, our algorithm synchronizes a set of videos in order to show the same gesture being performed at different speed. We believe that the proposed approach is a valuable addition to the existing learning tools for surgery.