Computational Modeling of Cardiac Ablation Incorporating Electrothermomechanical Interactions
Bibliographic record
Abstract
Abstract The application of radio frequency ablation (RFA) has been widely explored in treating various types of cardiac arrhythmias. Computational modeling provides a safe and viable alternative to ex vivo and in vivo experimental studies for quantifying the effects of different variables efficiently and reliably, apart from providing a priori estimates of the ablation volume attained during cardiac ablation procedures. In this contribution, we report a fully coupled electrothermomechanical model for a more accurate prediction of the treatment outcomes during the radio frequency cardiac ablation. A numerical model comprising of cardiac tissue and the cardiac chamber has been developed in which an electrode has been inserted perpendicular to the cardiac tissue to simulate actual clinical procedures. Temperature-dependent heat capacity, electrical and thermal conductivities, and blood perfusion rate have been considered to model more realistic scenarios. The effects of blood flow and contact force of the electrode tip on the treatment outcomes of a fully coupled model of RFA have been systematically investigated. The numerical study demonstrates that the predicted ablation volume of RFA is significantly dependent on the blood flow rate in the cardiac chamber and also on the tissue deformation induced due to electrode insertion depth of 1.5 mm or higher.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".