The growing interest in analysis of surgical video through machine learning has led to increased research efforts; however, common methods of annotating video data are lacking. There is a need to establish recommendations on the
annotation of surgical video data to enable assessment of algorithms and multi-institutional collaboration.
Four working groups were formed from a pool of participants that included clinicians, engineers, and data scientists. The working groups were focused on four themes: (1) temporal models, (2) actions and tasks, (3) tissue characteristics and general anatomy, and (4) software and data structure. A modified Delphi process was utilized to create a
consensus survey based on suggested recommendations from each of the working groups.
After three Delphi rounds,
consensus was reached on recommendations for
annotation within each of these domains. A hierarchy for
annotation of temporal events in surgery was established.
While additional work remains to achieve accepted standards for video annotation in surgery, the
consensus recommendations on a general framework for
annotation presented here lay the foundation for standardization. This type of framework is critical to enabling diverse datasets, performance benchmarks, and collaboration.