A robotics-assisted catheter manipulation system for cardiac ablation with real-time force estimation
Bibliographic record
Abstract
Lack of dexterous control over the position of a catheter's distal tip and not having any feedback from the quality of tip - tissue contact are among the factors that make the conventional catheter-based method of performing cardiac ablation very challenging. To resolve these issues, in this paper, we present a robotic catheter manipulation system that accommodates a conventional ablation catheter, places the ablation tip at the desired target and reports the forces that the tip exerts on the environment in real-time. In this system, the manual proximal handle is replaced with a mechanism that is capable of measuring the tension force along the pull-wire while actuating it to flex the distal shaft of the catheter. The placement of force/pressure sensors at the distal end of the catheter is avoided by developing a model-based force estimation technique using the measured tension force and information on the position and orientation of the distal tip. The developed system is further enhanced with an interface to assist the user in placing the catheter tip at the desired location while providing him/her with a real-time measure of the contact force. Extensive experiments show that using the proposed robotic system, the catheter tip is positioned within ±1 mm of the designated target and contact forces are reported in real-time with an accuracy of 3 gf.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".