Evidence map›Paper›PMID 31350704›Full record

ArticleInternational journal of computer assisted radiology and surgery2019

Co-localized augmented human and X-ray observers in collaborative surgical ecosystem.

Javad Fotouhi, Mathias Unberath, Tianyu Song, Jonas Hajek, Sing Chun Lee, Bastian Bier, Andreas Maier, Greg Osgood, Mehran Armand, Nassir Navab

Abstract read
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In one paragraph

Article in International journal of computer assisted radiology and surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
2.2field-weighted citation impact, top 13% 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

9 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Application of mixed reality-based surgical navigation system in craniomaxillofacial trauma bone reconstruction.Hua xi kou qiang yi xue za zhi = Huaxi kouqiang yixue zazhi = West China journal of stomatology · 2022
    Article
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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

10 authors at 4 institutions in 2 countries.

Javad FotouhiComputer Aided Medical Procedures, Johns Hopkins University, Baltimore, USA. javad.fotouhi@jhu.edu.ORCID http://orcid.org/0000-0001-8705-1764
Mathias UnberathComputer Aided Medical Procedures, Johns Hopkins University, Baltimore, USA.
Tianyu SongComputer Aided Medical Procedures, Johns Hopkins University, Baltimore, USA.
Jonas HajekComputer Aided Medical Procedures, Johns Hopkins University, Baltimore, USA.
Sing Chun LeeComputer Aided Medical Procedures, Johns Hopkins University, Baltimore, USA.
Bastian BierComputer Aided Medical Procedures, Johns Hopkins University, Baltimore, USA.
Andreas MaierPattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Greg OsgoodDepartment of Orthopedic Surgery, Johns Hopkins Hospital, Baltimore, USA.
Mehran ArmandApplied Physics Laboratory, Johns Hopkins University, Baltimore, USA.
Nassir NavabComputer Aided Medical Procedures, Johns Hopkins University, Baltimore, USA.
Johns Hopkins University · USFriedrich-Alexander-Universität Erlangen-Nürnberg · DEJohns Hopkins Hospital · USJohns Hopkins University Applied Physics Laboratory · US

Funding

NIH HHS EB016703NIH HHS EB020113NIH HHS EB023939
6 · The paper itself

Abstract

purposeImage-guided percutaneous interventions are safer alternatives to conventional orthopedic and trauma surgeries. To advance surgical tools in complex bony structures during these procedures with confidence, a large number of images is acquired. While image-guidance is the de facto standard to guarantee acceptable outcome, when these images are presented on monitors far from the surgical site the information content cannot be associated easily with the 3D patient anatomy.

methodsIn this article, we propose a collaborative augmented reality (AR) surgical ecosystem to jointly co-localize the C-arm X-ray and surgeon viewer. The technical contributions of this work include (1) joint calibration of a visual tracker on a C-arm scanner and its X-ray source via a hand-eye calibration strategy, and (2) inside-out co-localization of human and X-ray observers in shared tracking and augmentation environments using vision-based simultaneous localization and mapping.

resultsWe present a thorough evaluation of the hand-eye calibration procedure. Results suggest convergence when using 50 pose pairs or more. The mean translation and rotation errors at convergence are 5.7 mm and [Formula: see text], respectively. Further, user-in-the-loop studies were conducted to estimate the end-to-end target augmentation error. The mean distance between landmarks in real and virtual environment was 10.8 mm.

conclusionsThe proposed AR solution provides a shared augmented experience between the human and X-ray viewer. The collaborative surgical AR system has the potential to simplify hand-eye coordination for surgeons or intuitively inform C-arm technologists for prospective X-ray view-point planning.

Indexed as

Augmented RealityX-RaysAlgorithmsCalibrationEquipment DesignFluoroscopyHumansImaging, Three-DimensionalModels, StatisticalMotor SkillsProspective StudiesRadiographic Image Interpretation, Computer-AssistedRadiographySurgery, Computer-AssistedTomography, X-Ray ComputedAugmented realityCalibrationC-armSurgeryX-ray

Identifiers

PMID31350704
OpenAlexW2963854622

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.