dc.contributor.author | Meireles, Ozanan R. | |
dc.contributor.author | Rosman, Guy | |
dc.contributor.author | Altieri, Maria S. | |
dc.contributor.author | Carin, Lawrence | |
dc.contributor.author | Hager, Gregory | |
dc.contributor.author | Madani, Amin | |
dc.contributor.author | Padoy, Nicolas | |
dc.contributor.author | Pugh, Carla M. | |
dc.contributor.author | Sylla, Patricia | |
dc.contributor.author | Ward, Thomas M. | |
dc.contributor.author | Hashimoto, Daniel A. | |
dc.date.accessioned | 2021-11-01T14:33:50Z | |
dc.date.available | 2021-11-01T14:33:50Z | |
dc.date.issued | 2021-07-06 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/136860 | |
dc.description.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. | en_US |
dc.publisher | Springer US | en_US |
dc.relation.isversionof | https://doi.org/10.1007/s00464-021-08578-9 | en_US |
dc.rights | 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. | en_US |
dc.source | Springer US | en_US |
dc.title | SAGES consensus recommendations on an annotation framework for surgical video | en_US |
dc.type | Article | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory | |
dc.eprint.version | Author's final manuscript | en_US |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
dc.date.updated | 2021-08-07T03:38:54Z | |
dc.language.rfc3066 | en | |
dc.rights.holder | The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature | |
dspace.embargo.terms | Y | |
dspace.date.submission | 2021-08-07T03:38:54Z | |
mit.license | PUBLISHER_POLICY | |
mit.metadata.status | Authority Work and Publication Information Needed | |