Evidence map›Paper›PMID 33237461›Full record

ArticleSurgical endoscopy2021

Deep learning for surgical phase recognition using endoscopic videos.

Annetje C P Guédon, Senna E P Meij, Karim N M M H Osman, Helena A Kloosterman, Karlijn J van Stralen, Matthijs C M Grimbergen, Quirijn A J Eijsbouts, John J van den Dobbelsteen, Andru P Twinanda

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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 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

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

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  15. Pelphix: Surgical Phase Recognition from X-ray Images in Percutaneous Pelvic Fixation.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023
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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

9 authors.

Annetje C P GuédonDepartment of Clinical Physics, Spaarne Gasthuis, Spaarnepoort 1, 2134TM, Hoofddorp, the Netherlands. aguedon@spaarnegasthuis.nl.ORCID 0000-0001-8165-7802
Senna E P MeijDepartment of Biomechanical Engineering, Delft University of Technology, Delft, the Netherlands.
Karim N M M H OsmanDepartment of Biomechanical Engineering, Delft University of Technology, Delft, the Netherlands.
Helena A KloostermanCosmonio, Leeuwarden, the Netherlands.
Karlijn J van StralenSpaarne Gasthuis Academie, Spaarne Gasthuis, Hoofddorp, the Netherlands.
Matthijs C M GrimbergenDepartment of Radiology, Amsterdam UMC, Amsterdam, the Netherlands.
Quirijn A J EijsboutsDepartment of Surgery, Spaarne Gasthuis, Hoofddorp, the Netherlands.
John J van den DobbelsteenDepartment of Biomechanical Engineering, Delft University of Technology, Delft, the Netherlands.
Andru P TwinandaCosmonio, Leeuwarden, the Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOperating room planning is a complex task as pre-operative estimations of procedure duration have a limited accuracy. This is due to large variations in the course of procedures. Therefore, information about the progress of procedures is essential to adapt the daily operating room schedule accordingly. This information should ideally be objective, automatically retrievable and in real-time. Recordings made during endoscopic surgeries are a potential source of progress information. A trained observer is able to recognize the ongoing surgical phase from watching these videos. The introduction of deep learning techniques brought up opportunities to automatically retrieve information from surgical videos. The aim of this study was to apply state-of-the art deep learning techniques on a new set of endoscopic videos to automatically recognize the progress of a procedure, and to assess the feasibility of the approach in terms of performance, scalability and practical considerations.

methodsA dataset of 33 laparoscopic cholecystectomies (LC) and 35 total laparoscopic hysterectomies (TLH) was used. The surgical tools that were used and the ongoing surgical phases were annotated in the recordings. Neural networks were trained on a subset of annotated videos. The automatic recognition of surgical tools and phases was then assessed on another subset. The scalability of the networks was tested and practical considerations were kept up.

resultsThe performance of the surgical tools and phase recognition reached an average precision and recall between 0.77 and 0.89. The scalability tests showed diverging results. Legal considerations had to be taken into account and a considerable amount of time was needed to annotate the datasets.

conclusionThis study shows the potential of deep learning to automatically recognize information contained in surgical videos. This study also provides insights in the applicability of such a technique to support operating room planning.

Indexed as

Cholecystectomy, LaparoscopicDeep LearningLaparoscopyHumansNeural Networks, ComputerAutomatic recognitionDeep learningEndoscopic videosSurgical phase

Identifiers

PMID33237461

What OpenQuestion holds

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Read underepoch 390

Registered trials

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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.