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SAGES consensus recommendations on an annotation framework for surgical video

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.; Hashimoto, Daniel A.; ... Show more Show less
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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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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.
Date issued
2021-07-06
URI
https://hdl.handle.net/1721.1/136860
Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Publisher
Springer US

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