Model-based Force Estimation for Intra-cardiac Catheters
Bibliographic record
Abstract
The objectives of this paper are twofold: first, presenting an efficient quasi-static model for intracardiac catheters, and second, proposing a real-time approach for the estimation of the force at the tip using the proposed model. The force estimation approach has the potential to be used in conjunction with previously developed pose sensing technologies to provide a sense of the forces applied by the catheter to the heart tissue in cardiac ablation procedures. The catheter is modeled as a planar elastica, consisting of consecutive circular curves. The parameters of the model are obtained through experiments, leading to a precise description of the shape of the catheter for the given external forces. The approach incorporates modeling of the compound system (distal shaft and catheter body) with inhomogeneous mechanical properties. The force estimation approach is based on the pose measurement of the catheter tip along with the identified parameters of the catheter model. As a result of the low dimensionality of the proposed quasi-static model, the inverse problem can be efficiently solved for the estimation of the external forces. The experiments performed using electromagnetic sensors verify the feasibility of the proposed scheme in medical applications.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".