Evidence map›Paper›PMID 32691206›Full record

ArticleSurgical endoscopy2020

A case series study of augmented reality in laparoscopic liver resection with a deformable preoperative model.

Le Roy Bertrand, Mourad Abdallah, Yamid Espinel, Lilian Calvet, Bruno Pereira, Erol Ozgur, Denis Pezet, Emmanuel Buc, Adrien Bartoli

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In one paragraph

Article in Surgical endoscopy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
3.7field-weighted citation impact, top 6% of its field
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

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.

  1. Pooled it
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  7. Review
  8. Automatic, global registration in laparoscopic liver surgery.International journal of computer assisted radiology and surgery · 2022
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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 at 1 institution in 1 country.

Le Roy BertrandUMR6602, Endoscopy and Computer Vision Group, Faculté de Médecine, Institut Pascal, Bâtiment 3C, 28 place Henri Dunant, 63000, Clermont-Ferrand, France. leroybertrand8@gmail.com.ORCID http://orcid.org/0000-0002-4668-9275
Mourad AbdallahUMR6602, Endoscopy and Computer Vision Group, Faculté de Médecine, Institut Pascal, Bâtiment 3C, 28 place Henri Dunant, 63000, Clermont-Ferrand, France.
Yamid EspinelUMR6602, Endoscopy and Computer Vision Group, Faculté de Médecine, Institut Pascal, Bâtiment 3C, 28 place Henri Dunant, 63000, Clermont-Ferrand, France.
Lilian CalvetUMR6602, Endoscopy and Computer Vision Group, Faculté de Médecine, Institut Pascal, Bâtiment 3C, 28 place Henri Dunant, 63000, Clermont-Ferrand, France.
Bruno PereiraDélégation à la Recherche Clinique et à l'innovation, Hopital Gabriel Montpied, CHU de Clermont-Ferrand, Place Henri Dunand, 63000, Clermont-Ferrand, France.
Erol OzgurUMR6602, Endoscopy and Computer Vision Group, Faculté de Médecine, Institut Pascal, Bâtiment 3C, 28 place Henri Dunant, 63000, Clermont-Ferrand, France.
Denis PezetUMR6602, Endoscopy and Computer Vision Group, Faculté de Médecine, Institut Pascal, Bâtiment 3C, 28 place Henri Dunant, 63000, Clermont-Ferrand, France.
Emmanuel BucUMR6602, Endoscopy and Computer Vision Group, Faculté de Médecine, Institut Pascal, Bâtiment 3C, 28 place Henri Dunant, 63000, Clermont-Ferrand, France.
Adrien BartoliUMR6602, Endoscopy and Computer Vision Group, Faculté de Médecine, Institut Pascal, Bâtiment 3C, 28 place Henri Dunant, 63000, Clermont-Ferrand, France.
Institut Pascal · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrevious work in augmented reality (AR) guidance in monocular laparoscopic hepatectomy requires the surgeon to manually overlay a rigid preoperative model onto a laparoscopy image. This may be fairly inaccurate because of significant liver deformation. We have proposed a technique which overlays a deformable preoperative model semi-automatically onto a laparoscopic image using a new software called Hepataug. The aim of this study is to show the feasibility of Hepataug to perform AR with a deformable model in laparoscopic hepatectomy.

methodsWe ran Hepataug during the procedures, as well as the usual means of laparoscopic ultrasonography (LUS) and visual inspection of the preoperative CT or MRI. The primary objective was to assess the feasibility of Hepataug, in terms of minimal disruption of the surgical workflow. The secondary objective was to assess the potential benefit of Hepataug, by subjective comparison with LUS.

resultsFrom July 2017 to March 2019, 17 consecutive patients were included in this study. AR was feasible in all procedures, with good correlation with LUS. However, for 2 patients, LUS did not reveal the location of the tumors. Hepataug gave a prediction of the tumor locations, which was confirmed and refined by careful inspection of the preoperative CT or MRI.

conclusionHepataug showed a minimal disruption of the surgical workflow and can thus be feasibly used in real hepatectomy procedures. Thanks to its new mechanism of semi-automatic deformable alignment, Hepataug also showed a good agreement with LUS and visual CT or MRI inspection in subsurface tumor localization. Importantly, Hepataug yields reproducible results. It is easy to use and could be deployed in any existing operating room. Nevertheless, comparative prospective studies are needed to study its efficacy.

Indexed as

Augmented RealityLaparoscopyModels, BiologicalPreoperative CareAdultAgedAged, 80 and overFemaleHepatectomyHumansImaging, Three-DimensionalLiverMagnetic Resonance ImagingMaleMiddle AgedTomography, X-Ray ComputedAugmented realityDeformable 3D modelLaparoscopyLiverOverlayResection

Identifiers

PMID32691206
OpenAlexW3044127452

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.