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Record W3138505695

Automatic C-arm Positioning Using Multi-Functional User Interface

2019· article· en· W3138505695 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCMBES Proceedings · 2019
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsWorkflowInterface (matter)Computer scienceComputer visionPosition (finance)VisualizationKinematicsWorkspaceInverse kinematicsGround truthArtificial intelligenceDegrees of freedom (physics and chemistry)Robotic armUser interfaceSimulationRobot
DOInot available

Abstract

fetched live from OpenAlex

C-arm positioning is a critical step of the surgical workflow. The traditional method is often time consuming and results in additional radiation exposure to the patient and surgical staff. We propose a user interface that allows surgeons to interact with a simulated X-ray 3D reconstruction of the patient’s anatomy. Optimal views chosen by the surgeon with the simulated X-ray are used to calculate the C-arm position required to achieve that view. The proposed system uses pre-operative CT data to generate a 3D model, and inverse kinematics with 6 degrees of freedom to calculate the C-arm joint parameters. Day of surgery patient position variations are factored in through registration methods using the Kinect. Quantitative results were validated by comparing outputs with ground truths, and results indicate our method can output C-arm position values close to the truth considering the limitation of working with truncated values. Automatic positioning reduces radiation by minimizing typical positioning errors. Future work will include the integration of radiation exposure measurements and visualization into the user interface.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.703
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.047
GPT teacher head0.317
Teacher spread0.271 · 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