MétaCan
Menu
Back to cohort
Record W1981822312 · doi:10.1109/robot.2010.5509710

Development of a pneumatic robot for MRI-guided transperineal prostate biopsy and brachytherapy: New approaches

2010· article· en· W1981822312 on OpenAlexaff
Sang‐Eun Song, Nathan B. Cho, Gregory S. Fischer, N. Hata, Clare M. Tempany, Gábor Fichtinger, Iulian Iordachita

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsQueen's University
FundersNational Center for Research ResourcesNational Cancer Institute
KeywordsBrachytherapyWorkspaceRobotComputer scienceMagnetic resonance imagingModular designSimulationArtificial intelligenceRadiologyRadiation therapyMedicine

Abstract

fetched live from OpenAlex

Magnetic Resonance Imaging (MRI) guided prostate biopsy and brachytherapy has been introduced in order to enhance the cancer detection and treatment. For the accurate needle positioning, a number of robotic assistants have been developed. However, problems exist due to the strong magnetic field and limited workspace. Pneumatically actuated robots have shown the minimum distraction in the environment but the confined workspace limits optimal robot design and thus controllability is often poor. To overcome the problem, a simple external damping mechanism using timing belts was sought and a 1-DOF mechanism test result indicated sufficient positioning accuracy. Based on the damping mechanism and modular system design approach, a new workspace-optimized 4-DOF parallel robot was developed for the MRI-guided prostate biopsy and brachytherapy. A preliminary evaluation of the robot was conducted using previously developed pneumatic controller and satisfying results were obtained.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.243
Teacher spread0.199 · 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 designBench or experimental
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

Citations101
Published2010
Admission routes1
Has abstractyes

Explore more

Same topicSoft Robotics and ApplicationsFrench-language works237,207