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Record W2109413206 · doi:10.1080/13645700600771645

Control of soft tissue deformation during robotic needle insertion

2006· article· en· W2109413206 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

VenueMinimally Invasive Therapy & Allied Technologies · 2006
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsDeformation (meteorology)Soft tissueIndentationBiomedical engineeringDisplacement (psychology)TrajectoryRotation (mathematics)Materials scienceComputer scienceSurgeryMedicineArtificial intelligencePhysicsComposite material

Abstract

fetched live from OpenAlex

Accurate needle insertion into soft, inhomogeneous tissue is of practical interest because of its importance in 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 needle insertion. In order to reduce this deformation, we have studied the effect of different trajectories for a 2-DOF (degrees of freedom) robot performing needle insertion in soft tissue. To obtain an optimum trajectory, we have compared tissue indentation and frictional forces for different trajectories. According to the results of our experiments, infinitesimal force per tissue displacement is a useful parameter for online trajectory update. In addition, the results show that axial rotation can reduce tissue indentation before puncture and frictional forces after puncture. Our proposed position/force controller is shown to provide considerable improvement in performance with regard to minimizing 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.760

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 head0.009
GPT teacher head0.197
Teacher spread0.189 · 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