SAGES consensus recommendations on an annotation framework for surgical video
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464_2021_8578_ReferencePDF.pdf
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Author(s)
Meireles, Ozanan R.
Rosman, Guy
Altieri, Maria S.
Carin, Lawrence
Hager, Gregory
Madani, Amin
Padoy, Nicolas
Pugh, Carla M.
Sylla, Patricia
Ward, Thomas M.
Date Issued
July 6, 2021
Publisher
Springer US
Version
Author's final manuscript
Abstract
Abstract
Background
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.
Methods
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.
Results
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.
Conclusions
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1007/s00464-021-08578-9
