Evidence map›Paper›PMID 29943226›Full record

ArticleInternational journal of computer assisted radiology and surgery2018

A radiation-free mixed-reality training environment and assessment concept for C-arm-based surgery.

Philipp Stefan, Séverine Habert, Alexander Winkler, Marc Lazarovici, Julian Fürmetz, Ulrich Eck, Nassir Navab

Abstract read
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In one paragraph

Article in International journal of computer assisted radiology and surgery, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

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

10 citing papers in PubMed.

  1. Trial
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  5. Article
  6. Simulation training to retool practicing orthopedic surgeons is rare.Global surgical education : journal of the Association for Surgical Education · 2023
    Review
  7. Article
  8. Article
  9. Article
  10. 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

7 authors.

Philipp StefanComputer Aided Medical Procedures (CAMP), Technische Universität München, Munich, Germany. philipp.stefan@tum.de.ORCID http://orcid.org/0000-0003-4386-6456
Séverine HabertComputer Aided Medical Procedures (CAMP), Technische Universität München, Munich, Germany. severine.habert@tum.de.ORCID http://orcid.org/0000-0001-7633-6078
Alexander WinklerComputer Aided Medical Procedures (CAMP), Technische Universität München, Munich, Germany.
Marc LazaroviciKlinikum der Universität München, Ludwig-Maximilians-Universität München, Munich, Germany.
Julian FürmetzKlinikum der Universität München, Ludwig-Maximilians-Universität München, Munich, Germany.
Ulrich EckComputer Aided Medical Procedures (CAMP), Technische Universität München, Munich, Germany.
Nassir NavabComputer Aided Medical Procedures (CAMP), Technische Universität München, Munich, Germany.

Funding

Deutsche Forschungsgemeinschaft NA 620/30-1
6 · The paper itself

Abstract

purposeThe discrepancy of continuously decreasing opportunities for clinical training and assessment and the increasing complexity of interventions in surgery has led to the development of different training and assessment options like anatomical models, computer-based simulators or cadaver trainings. However, trainees, following training, assessment and ultimately performing patient treatment, still face a steep learning curve.

methodsTo address this problem for C-arm-based surgery, we introduce a realistic radiation-free simulation system that combines patient-based 3D printed anatomy and simulated X-ray imaging using a physical C-arm. To explore the fidelity and usefulness of the proposed mixed-reality system for training and assessment, we conducted a user study with six surgical experts performing a facet joint injection on the simulator.

resultsIn a technical evaluation, we show that our system simulates X-ray images accurately with an RMSE of 1.85 mm compared to real X-ray imaging. The participants expressed agreement with the overall realism of the simulation, the usefulness of the system for assessment and strong agreement with the usefulness of such a mixed-reality system for training of novices and experts. In a quantitative analysis, we furthermore evaluated the suitability of the system for the assessment of surgical skills and gather preliminary evidence for validity.

conclusionThe proposed mixed-reality simulation system facilitates a transition to C-arm-based surgery and has the potential to complement or even replace large parts of cadaver training, to provide a safe assessment environment and to reduce the risk for errors when proceeding to patient treatment. We propose an assessment concept and outline the steps necessary to expand the system into a test instrument that provides reliable and justified assessments scores indicative of surgical proficiency with sufficient evidence for validity.

Indexed as

Models, AnatomicUser-Computer InterfaceCadaverClinical CompetenceHumansLearning CurveLumbar VertebraeMaleOrthopedic ProceduresPrinting, Three-DimensionalSimulation TrainingSurgery, Computer-AssistedTomography, X-Ray ComputedAssessmentC-arm-based spine surgery simulationComputer aided interventionsMixed-reality simulationTraining

Identifiers

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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.