Catheter kinematics and control to enhance cardiac ablation
Bibliographic record
Abstract
Catheter ablation is the preferred minimally invasive treatment for cardiac arrhythmias. Limited maneuverability of currently available catheters undermines the success of this treatment and subjects operations to prolonged repeated attempts to pace suspicious zones and ablate the arrhythmogenic substrates under ionizing radiation of fluoroscopy. To compensate for such inefficiencies, a control system that can replace operator's hand during the procedure is desired. This system should be able to direct catheter tip toward the ablation site and maintain its contact with the substrate during ablation, accelerating the process and enhancing its precision. To realize such a system, the first step is to kinematically model the catheter and to devise a control strategy to embed the kinematics of the catheter. This paper proposes a simplified approach to model and control a general single-segment active catheter as a continuum robot. In this approach, the flexible catheter is modeled as a rigid manipulator having coupled joints. Utilizing the structural coupling of the catheter, joint-variables are reduced to actuatable parameters thus lifting some of the mathematical difficulties in formulation of a control strategy for redundant manipulators. The modeling is validated through experiments with a typical steerable ablation catheter equipped with an electromagnetic tracker in vitro.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".