Abstract 10236: Prospective Assessment of ECG-Image-based Real-Time Individual Virtual-Heart Automatic Localization (RIVAL) for Scar-Related Ventricular Tachycardia (VT)
Bibliographic record
Abstract
Background: We developed a novel RIVAL system that consists of a CT-based computational simulation to identify VT circuits and an ECG-based automated approach to localize VT exit sites. Objective: Prospectively assess the ability of the RIVAL system to localize VT circuits and exits. Methods: Patients presenting for VT ablation were enrolled into the study. Pre-procedural cardiac CT was performed and used to conduct heart simulations for predicting VT circuits. The patient’s CT geometry with the predicted VT circuits was registered to the electroanatomic shell created during the procedure and imported into the RIVAL program. During the procedure, exit sites of induced VTs are localized in real-time using the 12-lead ECG onto the patient-specific CT surface. Predicted ablation regions obtained by combining RIVAL-predicted VT circuits with exit sites were analyzed offline to assess localization accuracy to the invasive VT ablation procedure. Results: Four patients with ischemic cardiomyopathy undergoing VT ablation had preprocedural cardiac CT. In P1, two VTs were induced during the procedure. The RIVAL predicted 2 VT circuits and achieved a mean localization error of 7.0mm for VT exit site prediction (Fig1). There was a spatial concordance between the predicted ablation areas and the clinical ablation regions. For P2, three VTs were induced during the VT ablation. A large area of scar was associated with 6 RIVAL-predicted VT circuits. The exit site localization accuracy could not be precisely quantitated because the VTs terminated with ablation at a mid-diastolic site. For P3, no VTs were inducible, however substrate modification (SM) was performed in the anterior-apical LV. The RIVAL predicted only one VT circuit located in the area where SM was performed. No VT was induced for P4 and the RIVAL did not predict any VT circuits. Conclusions: The RIVAL predicts the VT circuit and exit accurately, which may improve the precision of ablation therapies and procedure outcomes.
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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 | 0.001 | 0.003 |
| 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".