Design and Evaluation of a Catheter Contact-Force Controller for Cardiac Ablation Therapy
Bibliographic record
Abstract
GOAL: Maintaining a constant contact force (CF) of an ablation catheter during cardiac catheter ablation therapy is clinically challenging due to inherent myocardial motion, often resulting in poor ablation of arrhythmogenic substrates. To enable a prescribed contact force to be applied during ablation, a catheter contact force controller (CCFC) was developed. METHODS: The system includes a hand-held device attached to a commercial catheter and steerable sheath. A compact linear motor assembly attaches to an ablation catheter and autonomously controls its relative position within the shaft of the steerable sheath. A closed-loop control system is implemented within embedded electronics to enable real-time catheter-tissue contact force control. To evaluate the performance of the CCFC, a linear motion phantom was used to impose a series of physiological CF profiles; lesion CF was controlled at prescribed levels ranging from 15 to 40 g. RESULTS: For a prescribed CF of 25 g, the CCFC was able to regulate the CF with a root mean squared error of 3.7 ± 0.7 g. The ability of the CCFC to retract the catheter upon sudden changes in tissue motion, which may have caused tissue damage, was also demonstrated. Finally, the device was able to regulate the CF for a predetermined amount of time according to a force-time integral model. CONCLUSION: The developed CCFC is capable of regulating catheter-tissue CF in a laboratory setting that mimics clinical ablation therapy. SIGNIFICANCE: Catheter-tissue CF control promises to improve the precision and success of ablation lesion delivery.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".