Meireles, OR;
Rosman, G;
Altieri, MS;
Carin, L;
Hager, G;
Madani, A;
Padoy, N;
... Nguyen, H; + view all
(2021)
SAGES consensus recommendations on an annotation framework for surgical video.
Surgical Endoscopy
, 35
pp. 4918-4929.
10.1007/s00464-021-08578-9.
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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.
| Type: | Article |
|---|---|
| Title: | SAGES consensus recommendations on an annotation framework for surgical video |
| Location: | Germany |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s00464-021-08578-9 |
| Publisher version: | https://doi.org/10.1007/s00464-021-08578-9 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Annotation, Artificial intelligence, Computer vision, Consensus, Minimally invasive surgery, Surgical video, Consensus, Delphi Technique, Humans, Machine Learning, Surveys and Questionnaires |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10153663 |
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