Dynamic Modeling and Identification of a Robotic Intracardiac Echo Catheter
Bibliographic record
Abstract
Catheter-based cardiac ablation is the preferred method of treating atrial fibrillation. Conventionally, the catheter is navigated in the heart using X-ray fluoroscopy imaging and an electroanatomical map. Although successful, these imaging modalities do not provide real-time feedback on the quality of lesions created, which in turn could lead to recurrence of arrhythmia. Intracardiac echo (ICE) catheter provides real-time imaging within the heart to visualize both the ablation catheter and lesions created. However, manipulating the ablation and ICE catheters simultaneously is tedious and time consuming. As a first step towards developing a robotic ICE catheter that can autonomously follow the ablation catheter and monitor the lesions, we have developed a dynamic model for the ICE catheter. The model is based on the Cosserat theory for flexible rods that relies on strain parametrization. The model also accounts for frictional forces between the catheter sheath and tendons, external loads and fluid forces acting on the catheter. A good nominal model for describing the catheter dynamics is essential to develop a robust control scheme for the robotic ICE catheter. The parameters of the ICE catheter are estimated using weight release, tendon-driven actuation and fluid flow experiments. To the best of our knowledge, this is the first dynamic model for the ICE catheter that accurately reflects the dynamics of the catheter under pulsatile fluid flow within a heart phantom.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".