Optimal transseptal puncture location for robot‐assisted left atrial catheter ablation
Bibliographic record
Abstract
BACKGROUND: The preferred method of treatment for atrial fibrillation (AF) is by catheter ablation, in which a catheter is guided into the left atrium through a transseptal puncture. However, the transseptal puncture constrains the catheter, thereby limiting its manoeuvrability and increasing the difficulty in reaching various locations in the left atrium. In this paper, we address the problem of choosing the optimal transseptal puncture location for performing cardiac ablation to obtain maximum manoeuvrability of the catheter. METHODS: We have employed an optimization algorithm to maximize the global isotropy index (GII) to evaluate the optimal transseptal puncture location. As part of this algorithm, a novel kinematic model for the catheter has been developed, based on a continuum robot model. Pre-operative MR/CT images of the heart are segmented using the open source image-guided therapy software, 3D Slicer, to obtain models of the left atrium and septal wall. These models are input to the optimization algorithm to evaluate the optimal transseptal puncture location. RESULTS: The continuum robot model accurately describes the kinematics of the catheter. Simulation and experimental results for the optimal transseptal puncture location are presented in this paper. The optimization algorithm generates discrete points on the septal wall for which the dexterity of the catheter in the left atrium is maximum, corresponding to a GII of 0.4362. CONCLUSION: We have developed an optimization algorithm based on the GII to evaluate the optimal position of the transseptal puncture for left atrial cardiac ablation.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".