Displacement-based Model for Estimation of Contact Force Between RFA Catheter and Atrial Tissue with ex-vivo Validation
Bibliographic record
Abstract
The goal of this study was to investigate the validity of a new contact model for sensor-less estimation of the contact force between RFA catheter and atrial tissue. To this end, a new nonlinear displacement-based model was proposed. Also, the model was formulated for forward and inverse problems and two solution schema were proposed. For assessing the model performance, two dynamic ex-vivo indentation tests were performed on a freshly excised porcine atrium, i.e. one with sinusoidal and one with triangular indentations. Results of the first test were used for parameter identification and verification of the model, while the second test was the benchmark for the model validation. Displacement range for both tests was 2±1mm, while the frequencies of indentations were 1, 1.5 and 2Hz for the sinusoidal, and 1.25Hz for the triangular indentation. From the sinusoidal test results, model parameters were identified using a particle-swarm optimization method. Using the optimized parameters, experimental forces were reconstructed. Error analysis revealed that the model was 91.5 % accurate in repeating the results of the sinusoidal test. Also, validation results showed an accuracy of 90.9% in model predictions of the contact force in the triangular test. Furthermore, the model could successfully capture the stress relaxation phenomenon, which is of high prominence in contact force control on atrial tissue. In conclusion, this investigation confirmed that the proposed contact model was valid in prediction of dynamic contact force with atrial tissue. Also, the proposed solution schema was fast-enough to be used in real-time surgical 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".