A real-time spatiotemporal AI model analyzes skill in open surgical videos
This work addresses the problem of optimizing surgical practice and improving patient outcomes for open surgery, which is the dominant form worldwide, by providing a dataset and model for AI development, though it is incremental as it extends existing AI methods to a new domain.
The authors tackled the lack of AI models for open surgical videos by curating the largest dataset from YouTube (1997 videos across 23 procedures) and developing a multi-task AI model for real-time analysis of surgical behaviors, hands, and tools, which generalized to prospectively collected data and identified kinematic descriptors of skill related to hand motion efficiency.
Open procedures represent the dominant form of surgery worldwide. Artificial intelligence (AI) has the potential to optimize surgical practice and improve patient outcomes, but efforts have focused primarily on minimally invasive techniques. Our work overcomes existing data limitations for training AI models by curating, from YouTube, the largest dataset of open surgical videos to date: 1997 videos from 23 surgical procedures uploaded from 50 countries. Using this dataset, we developed a multi-task AI model capable of real-time understanding of surgical behaviors, hands, and tools - the building blocks of procedural flow and surgeon skill. We show that our model generalizes across diverse surgery types and environments. Illustrating this generalizability, we directly applied our YouTube-trained model to analyze open surgeries prospectively collected at an academic medical center and identified kinematic descriptors of surgical skill related to efficiency of hand motion. Our Annotated Videos of Open Surgery (AVOS) dataset and trained model will be made available for further development of surgical AI.