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Virtual Fluoroscopy: Computer-Assisted Fluoroscopic Navigation

2001· article· en· W2089279779 on OpenAlexaff
Kevin T. Foley, David Simón, Y. Raja Rampersaud

Bibliographic record

VenueSpine · 2001
Typearticle
Languageen
FieldMedicine
TopicSpinal Fractures and Fixation Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFluoroscopyMedicineCadaverVirtual imageDosimeterNuclear medicineComputer visionRadiologyComputer scienceSurgeryDosimetry

Abstract

fetched live from OpenAlex

STUDY DESIGN: In vitro accuracy assessment of a novel virtual fluoroscopy system. OBJECTIVES: To investigate a new technology combining image-guided surgery with C-arm fluoroscopy. SUMMARY OF BACKGROUND DATA: Fluoroscopy is a useful and familiar technology to all musculoskeletal surgeons. Its limitations include radiation exposure to the patient and operating team and the need to reposition the fluoroscope repeatedly to obtain surgical guidance in multiple planes. METHODS: Fluoroscopic images of the lumbar spine of an intact, unembalmed cadaver were obtained, calibrated, and saved to an ). A was used for the sequential insertion of a light-emitting diode-fitted probe into the pedicles of L1-S1 bilaterally. The trajectory of a "virtual tool" corresponding to the tracked tool was overlaid onto the saved fluoroscopic views in real time. Live fluoroscopic images of the inserted pedicle probe were then obtained. Distances between the tips of the virtual and fluoroscopically displayed probes were quantified using the image-guided computer's measurement tool. Trajectory angle differences were measured using a standard goniometer and printed copies of the workstation computer display. The surgeon's radiation exposure was measured using thermolucent dosimeter rings. RESULTS: Excellent correlation between the virtual fluoroscopic images and live fluoroscopy was observed. Mean probe tip error was 0.97 +/- 0.40 mm. Mean trajectory angle difference between the virtual and fluoroscopically displayed probes was 2.7 degrees +/- 0.6 degrees. The thermolucent dosimeter rings measured no detectable radiation exposure for the surgeon. CONCLUSIONS: Virtual fluoroscopy offers several advantages over conventional fluoroscopy while providing acceptable targeting accuracy. It enables a single C-arm to provide real-time, multiplanar procedural guidance. It also dramatically reduces radiation exposure to the patient and surgical team by eliminating the need for repetitive fluoroscopic imaging for tool placement.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.017
GPT teacher head0.310
Teacher spread0.293 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations256
Published2001
Admission routes1
Has abstractyes

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