Evidence map›Paper›PMID 39413048›Full record

ArticleBJS open2024

Automated analysis of operative video in surgical training: scoping review.

Lachlan Dick, Connor P Boyle, Richard J E Skipworth, Douglas S Smink, Victoria Ruth Tallentire, Steven Yule

Abstract readScoping Review
In one paragraph

Article in BJS open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

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

  1. Pooled it
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  6. Review
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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

6 authors.

Lachlan DickSurgical Sabermetrics Laboratory, Usher Institute, University of Edinburgh, Edinburgh, UK.ORCID 0000-0002-4341-9501
Connor P BoyleDepartment of Surgery, Royal Infirmary of Edinburgh, Edinburgh, UK.
Richard J E SkipworthSurgical Sabermetrics Laboratory, Usher Institute, University of Edinburgh, Edinburgh, UK.
Douglas S SminkDepartment of Surgery, Brigham and Women's Hospital/Harvard Medical School, Boston, Massachusetts, USA.
Victoria Ruth TallentireMedical Education Directorate, NHS Lothian, Edinburgh, UK.
Steven YuleSurgical Sabermetrics Laboratory, Usher Institute, University of Edinburgh, Edinburgh, UK.ORCID 0000-0001-9889-9090

Funding

NHS LothianNHS Research Scotland (NRS)
6 · The paper itself

Abstract

backgroundThere is increasing availability of operative video for use in surgical training. Emerging technologies can now assess video footage and automatically generate metrics that could be harnessed to improve the assessment of operative performance. However, a comprehensive understanding of which technology features are most impactful in surgical training is lacking. The aim of this scoping review was to explore the current use of automated video analytics in surgical training.

methodsPubMed, Scopus, the Web of Science, and the Cochrane database were searched, to 29 September 2023, following PRISMA extension for scoping reviews (PRISMA-ScR) guidelines. Search terms included 'trainee', 'video analytics', and 'education'. Articles were screened independently by two reviewers to identify studies that applied automated video analytics to trainee-performed operations. Data on the methods of analysis, metrics generated, and application to training were extracted.

resultsOf the 6736 articles screened, 13 studies were identified. Computer vision tracking was the common method of video analysis. Metrics were described for processes (for example movement of instruments), outcomes (for example intraoperative phase duration), and critical safety elements (for example critical view of safety in laparoscopic cholecystectomy). Automated metrics were able to differentiate between skill levels (for example consultant versus trainee) and correlated with traditional methods of assessment. There was a lack of longitudinal application to training and only one qualitative study reported the experience of trainees using automated video analytics.

conclusionThe performance metrics generated from automated video analysis are varied and encompass several domains. Validation of analysis techniques and the metrics generated are a priority for future research, after which evidence demonstrating the impact on training can be established.

Indexed as

Clinical CompetenceVideo RecordingGeneral SurgeryHumansSurgical Procedures, Operative

Identifiers

PMID39413048
PMCPMC11482280

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.