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Record W2126153312 · doi:10.1109/iembs.2004.1403782

Trajectory generation for robotic needle insertion in soft tissue

2005· article· en· W2126153312 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsDeformation (meteorology)TrajectoryDisplacement (psychology)Soft tissueBrachytherapyRobotComputer scienceBiomedical engineeringProstate brachytherapyPercutaneousComputer visionArtificial intelligenceMaterials scienceSurgeryPhysicsEngineeringMedicineRadiation therapy

Abstract

fetched live from OpenAlex

Accurate needle insertion in soft, inhomogeneous tissue has been a major concern in several recent studies involving robot-assisted percutaneous therapies. In procedures that involve multiple needle insertions such as transrectal ultrasound guided prostate brachytherapy, it is important to reduce tissue deformation before puncture and during insertion. In order to reduce this deformation, we have studied the effect of different trajectories for a 2-DOF robot performing needle insertion in soft tissue. We have compared tissue deformation and infinitesimal force per tissue displacement for different trajectories. According to the results of our experiments, infinitesimal force per displacement is a useful parameter for online trajectory update. Our proposed position/force controller is shown to provide considerable improvement in performance with regard to tissue deformation before puncture.

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 score0.870
Threshold uncertainty score0.217

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 head0.024
GPT teacher head0.246
Teacher spread0.222 · 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

Quick stats

Citations44
Published2005
Admission routes1
Has abstractyes

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