Model-based Force Estimation of a Steerable Ablation Catheter in the Presence of the Blood Flow
Bibliographic record
Abstract
Catheter ablation (CA) is a well-established treatment for arrhythmia such as atrial fibrillation. Studies have shown that the rate of success of this procedure directly depends on the size, depth and shape of the lesions formed. Catheter tip-tissue contact force (CF) is known to be one of the key factors affecting lesion quality and therefore a major determinant of the outcome of the procedure. However, accurate knowledge of CF remains a major challenge due to the various complexities involved in the CA procedure. This paper presents the development of a kinetostatic model which can estimate CF in the presence of blood flow. The model, which is based on Euler-Bernoulli's beam theory to describe large deflection, is capable of reporting the tip contact force in real-time, where the effect of blood flow is approximated using the independence principle. The equation governing the static equilibrium of the catheter is formulated as a minimization problem and solved in MATLAB using the pseudo-inverse theorem, where the computational frequency of the force estimation was found to be 33.3 Hz. The proposed method is verified numerically using a finite element model, for various loading conditions, where the maximum relative error was found to be below 3%. Furthermore, a parametric study is conducted to investigate the effect of the blood flow on the catheter contact force estimation, for a catheter with various lengths, diameters, and bending stiffness.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".