Evidence map›Paper›PMID 29602988›Full record

ArticleSurgical endoscopy2018

Mobile, real-time, and point-of-care augmented reality is robust, accurate, and feasible: a prospective pilot study.

Hannes Götz Kenngott, Anas Amin Preukschas, Martin Wagner, Felix Nickel, Michael Müller, Nadine Bellemann, Christian Stock, Markus Fangerau, Boris Radeleff, Hans-Ulrich Kauczor and 3 more

Abstract readValidation Study
PubMed Publisher
In one paragraph

Article in Surgical endoscopy, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. [Robotics and augmented reality : Current state of development and future perspectives].Der Chirurg; Zeitschrift fur alle Gebiete der operativen Medizen · 2018
    Review
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

13 authors.

Hannes Götz KenngottDepartment of General, Visceral and Transplantation Surgery, Heidelberg University, Im Neuenheimer Feld 110, 69120, Heidelberg, Germany.
Anas Amin PreukschasDepartment of General, Visceral and Transplantation Surgery, Heidelberg University, Im Neuenheimer Feld 110, 69120, Heidelberg, Germany.
Martin WagnerDepartment of General, Visceral and Transplantation Surgery, Heidelberg University, Im Neuenheimer Feld 110, 69120, Heidelberg, Germany.
Felix NickelDepartment of General, Visceral and Transplantation Surgery, Heidelberg University, Im Neuenheimer Feld 110, 69120, Heidelberg, Germany.
Michael MüllerDivision of Medical and Biological Informatics, German Cancer Research Center, Heidelberg, Germany.
Nadine BellemannDepartment of Diagnostic and Interventional Radiology, Heidelberg University, Heidelberg, Germany.
Christian StockInstitute for Medical Biometry and Informatics, Heidelberg University, Heidelberg, Germany.
Markus FangerauDepartment of Diagnostic and Interventional Radiology, Heidelberg University, Heidelberg, Germany.
Boris RadeleffDepartment of Diagnostic and Interventional Radiology, Heidelberg University, Heidelberg, Germany.
Hans-Ulrich KauczorDepartment of Diagnostic and Interventional Radiology, Heidelberg University, Heidelberg, Germany.
Hans-Peter MeinzerDivision of Medical and Biological Informatics, German Cancer Research Center, Heidelberg, Germany.
Lena Maier-HeinDivision of Medical and Biological Informatics, German Cancer Research Center, Heidelberg, Germany.
Beat Peter Müller-StichDepartment of General, Visceral and Transplantation Surgery, Heidelberg University, Im Neuenheimer Feld 110, 69120, Heidelberg, Germany. beat.mueller@med.uni-heidelberg.de.ORCID 0000-0002-8552-8538

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAugmented reality (AR) systems are currently being explored by a broad spectrum of industries, mainly for improving point-of-care access to data and images. Especially in surgery and especially for timely decisions in emergency cases, a fast and comprehensive access to images at the patient bedside is mandatory. Currently, imaging data are accessed at a distance from the patient both in time and space, i.e., at a specific workstation. Mobile technology and 3-dimensional (3D) visualization of radiological imaging data promise to overcome these restrictions by making bedside AR feasible.

methodsIn this project, AR was realized in a surgical setting by fusing a 3D-representation of structures of interest with live camera images on a tablet computer using marker-based registration. The intent of this study was to focus on a thorough evaluation of AR. Feasibility, robustness, and accuracy were thus evaluated consecutively in a phantom model and a porcine model. Additionally feasibility was evaluated in one male volunteer.

resultsIn the phantom model (n = 10), AR visualization was feasible in 84% of the visualization space with high accuracy (mean reprojection error ± standard deviation (SD): 2.8 ± 2.7 mm; 95th percentile = 6.7 mm). In a porcine model (n = 5), AR visualization was feasible in 79% with high accuracy (mean reprojection error ± SD: 3.5 ± 3.0 mm; 95th percentile = 9.5 mm). Furthermore, AR was successfully used and proved feasible within a male volunteer.

conclusionsMobile, real-time, and point-of-care AR for clinical purposes proved feasible, robust, and accurate in the phantom, animal, and single-trial human model shown in this study. Consequently, AR following similar implementation proved robust and accurate enough to be evaluated in clinical trials assessing accuracy, robustness in clinical reality, as well as integration into the clinical workflow. If these further studies prove successful, AR might revolutionize data access at patient bedside.

Indexed as

Imaging, Three-DimensionalPoint-of-Care SystemsAnimalsFeasibility StudiesHumansMagnetic Resonance ImagingMaleModels, AnimalPhantoms, ImagingPilot ProjectsProspective StudiesSurgery, Computer-AssistedSwineTomography, X-Ray ComputedAugmented realityImage visualizationMobile deviceVisual assistance

Identifiers

PMID29602988

What OpenQuestion holds

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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.