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Record W2781038306 · doi:10.1002/rcs.1885

Camera‐augmented mobile C‐arm (CamC): A feasibility study of augmented reality imaging in the operating room

2017· article· en· W2781038306 on OpenAlexaff
Anna Maria von der Heide, Pascal Fallavollita, Lejing Wang, Philipp Sandner, Nassir Navab, Simon Weidert, Ekkehard Euler

Bibliographic record

VenueInternational Journal of Medical Robotics and Computer Assisted Surgery · 2017
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsWorkflowFluoroscopyProtocol (science)Radiation exposureComputer scienceAugmented realitySimulationMedical physicsMedicineNuclear medicineSurgeryArtificial intelligenceDatabase

Abstract

fetched live from OpenAlex

BACKGROUND: In orthopaedic trauma surgery, image-guided procedures are mostly based on fluoroscopy. The reduction of radiation exposure is an important goal. The purpose of this work was to investigate the impact of a camera-augmented mobile C-arm (CamC) on radiation exposure and the surgical workflow during a first clinical trial. METHODS: Applying a workflow-oriented approach, 10 general workflow steps were defined to compare the CamC to traditional C-arms. The surgeries included were arbitrarily identified and assigned to the study. The evaluation criteria were radiation exposure and operation time for each workflow step and the entire surgery. The evaluation protocol was designed and conducted in a single-centre study. RESULTS: The radiation exposure was remarkably reduced by 18 X-ray shots 46% using the CamC while keeping similar surgery times. CONCLUSIONS: The intuitiveness of the system, its easy integration into the surgical workflow, and its great potential to reduce radiation have been demonstrated.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.081
GPT teacher head0.389
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations21
Published2017
Admission routes1
Has abstractyes

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Same venueInternational Journal of Medical Robotics and Computer Assisted SurgerySame topicSurgical Simulation and TrainingFrench-language works237,207