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Record W2104767879 · doi:10.1109/tbme.2009.2013134

Catheter Kinematics for Intracardiac Navigation

2009· article· en· W2104767879 on OpenAlexafffund
Yusof Ganji, Farrokh Janabi‐Sharifi

Bibliographic record

VenueIEEE Transactions on Biomedical Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsToronto Metropolitan UniversityUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Waterloo
KeywordsKinematicsPosition (finance)Intracardiac injectionComputer scienceGoodness of fitWorkspaceSimulationArtificial intelligenceRobotSurgeryMachine learningMedicinePhysics

Abstract

fetched live from OpenAlex

Steerable catheters are utilized frequently in minimally invasive cardiac interventions. Despite their extensive applications, the properties of the steerable section of such devices have not been thoroughly investigated. In this paper, the kinematics of the distal shaft of the catheters is modeled, and the catheter's reachable workspace and its singular configurations are studied. The modeling is empirically validated through experiments with actual catheters mounted on a specialized robot. The statistical analysis of the experiments verify the effectiveness of the proposed model in estimating the catheter's tip position. In the experiments, the modeling error does not exceed 2.66 +/- 1.96 mm, and the mean absolute error in position coordinates is less than 1.55 mm. In addition, the linear relationship between the model and the measured position vectors is demonstrated, and the significance of the modeling goodness of fit is established. Based on the precision and computational effectiveness of the method, the applicability of the modeling to control and simulation purposes is postulated.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.008
GPT teacher head0.223
Teacher spread0.215 · 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
GenreEmpirical

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
Published2009
Admission routes2
Has abstractyes

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