Evidence map›Paper›PMID 34231065›Full record

ArticleSurgical endoscopy2021

SAGES consensus recommendations on an annotation framework for surgical video.

Ozanan R Meireles, Guy Rosman, Maria S Altieri, Lawrence Carin, Gregory Hager, Amin Madani, Nicolas Padoy, Carla M Pugh, Patricia Sylla, Thomas M Ward and 2 more

Abstract readConsensus Statement
PubMed Publisher
In one paragraph

Article in Surgical endoscopy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
54citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

54 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  5. Action classification for endoscopic pituitary adenoma resection: a consensus-based study.International journal of computer assisted radiology and surgery · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Ozanan R MeirelesDepartment of Surgery, Massachusetts General Hospital, 15 Parkman Street, WAC460, Boston, MA, 02114, USA. ozmeireles@mgh.harvard.edu.
Guy RosmanDepartment of Surgery, Massachusetts General Hospital, 15 Parkman Street, WAC460, Boston, MA, 02114, USA.
Maria S AltieriDepartment of Surgery, East Carolina University, Greenville, USA.
Lawrence CarinDepartment of Electrical and Computer Engineering, Duke University, Durham, USA.
Gregory HagerDepartment of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA.
Amin MadaniDepartment of Surgery, University Health Network, Toronto, Canada.
Nicolas PadoyICube, University of Strasbourg, Strasbourg, France.
Carla M PughDepartment of Surgery, Stanford University, Stanford, USA.
Patricia SyllaDepartment of Surgery, Mount Sinai Medical Center, New York, USA.
Thomas M WardDepartment of Surgery, Massachusetts General Hospital, 15 Parkman Street, WAC460, Boston, MA, 02114, USA.
Daniel A HashimotoDepartment of Surgery, Massachusetts General Hospital, 15 Parkman Street, WAC460, Boston, MA, 02114, USA. dahashimoto@mgh.harvard.edu.ORCID 0000-0003-4725-3104
SAGES Video Annotation for AI Working Groups

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Machine LearningDelphi TechniqueHumansSurveys and QuestionnairesAnnotationArtificial intelligenceComputer visionConsensusMinimally invasive surgerySurgical video

Identifiers

PMID34231065

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.