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Record W2151449437 · doi:10.1109/toh.2011.34

Haptic Simulator for Prostate Brachytherapy with Simulated Needle and Probe Interaction

2011· article· en· W2151449437 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Haptics · 2011
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversity of British Columbia
FundersNational Research Council Canada
KeywordsHaptic technologyMedical simulationComputer scienceFlexibility (engineering)SimulationBrachytherapyUltrasoundVirtual realityArtificial intelligenceRadiologyMedicine

Abstract

fetched live from OpenAlex

This paper presents a haptic simulator for prostate brachytherapy. Both needle insertion and the manipulation of the transrectal ultrasound (TRUS) probe are controlled via haptic devices. Tissue interaction forces that are computed by a deformable tissue model based on the finite element method (FEM) are rendered to the user by these devices. The needle insertion simulation employs 3D models of needle flexibility and asymmetric tip bevel. The needle-tissue simulation allows a trainee to practice needle insertion and targeting. The TRUS-tissue interaction simulation allows a trainee to practice the 3D intraoperative TRUS placement for registration with the preoperative volume study and to practice TRUS axial translation and rotation for imaging needles during insertions. Approaches to computational acceleration for realtime haptic performance are presented. Trade-offs between accuracy and speed are discussed. A graphics-card implementation of the numerically intensive mesh-adaptation operation is also presented. The simulator can be used for training, rehearsal, and treatment planning.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.868
Threshold uncertainty score0.585

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)0.0000.000

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 head0.026
GPT teacher head0.230
Teacher spread0.205 · 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