MétaCan
Menu
Back to cohort
Record W2526315379 · doi:10.1109/aim.2016.7576932

Black-box modeling and control of steerable ablation catheters

2016· article· en· W2526315379 on OpenAlexaff
Mahta Khoshnam, Peyman Yadmellat, Rajni V. Patel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsFocus (optics)Computer scienceAblationCatheterBiomedical engineeringBlack boxBendingSimulationControl theory (sociology)Artificial intelligencePhysicsSurgeryEngineeringControl (management)MedicineStructural engineering

Abstract

fetched live from OpenAlex

Minimally invasive intervention for treating cardiac arrhythmia involves ablating sources causing chaotic electrical signals in the atria. In order to perform tissue ablation, flexible catheters are steered through the vasculature from the insertion point in the groin area to the atrium. The outcome of this procedure depends on a number of factors, among which is the correct placement of the catheter tip on the target tissue. The tip/tissue contact angle is adjusted by using a manual prismatic knob on the proximal handle to bend the deflectable distal shaft of the catheter. With the goal of developing a robotics-assisted catheter manipulation system, in this paper, we focus on the problem of controlling the bending of the distal shaft when the proximal handle is used within a robotic manipulator. To this end, we apply the black-box modeling technique and perform an extensive experimental study to characterize the behavior of the catheter. The relationship between proximal measurements and the distal tip angle is then determined by fitting a model to the experimental dataset. The proposed model is used in designing a control system that adjusts the bending of the distal shaft merely based on proximal measurements with no feedback from the distal end. The performance of the designed control scheme is evaluated through experimental implementation. The results show that the control system can achieve the desired tip angle with an error less than 2 deg.

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 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 score0.794
Threshold uncertainty score0.089

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 head0.196
Teacher spread0.187 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations2
Published2016
Admission routes1
Has abstractyes

Explore more

Same topicSoft Robotics and ApplicationsFrench-language works237,207