SAGES consensus recommendations on an annotation framework for surgical video View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2021-07-06

AUTHORS

Ozanan R. Meireles, Guy Rosman, Maria S. Altieri, Lawrence Carin, Gregory Hager, Amin Madani, Nicolas Padoy, Carla M. Pugh, Patricia Sylla, Thomas M. Ward, Daniel A. Hashimoto

ABSTRACT

BackgroundThe 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.MethodsFour 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.ResultsAfter 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.ConclusionsWhile 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. More... »

PAGES

4918-4929

References to SciGraph publications

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  • 2018-07-13. Toward a standard ontology of surgical process models in INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY
  • 2020-07-27. Automated operative phase identification in peroral endoscopic myotomy in SURGICAL ENDOSCOPY
  • 2009. Data-Derived Models for Segmentation with Application to Surgical Assessment and Training in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2009
  • 2009. Task versus Subtask Surgical Skill Evaluation of Robotic Minimally Invasive Surgery in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2009
  • 2021-01-04. Deep learning visual analysis in laparoscopic surgery: a systematic review and diagnostic test accuracy meta-analysis in SURGICAL ENDOSCOPY
  • 2006. Recovery of Surgical Workflow Without Explicit Models in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2006
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00464-021-08578-9

    DOI

    http://dx.doi.org/10.1007/s00464-021-08578-9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1139449273

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/34231065


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