Abstract 14725: Image-based Virtual-heart Predictions Co-localize With ECG-based Automated Localization of Scar-related Ventricular Tachycardias
Bibliographic record
Abstract
Introduction: We previously developed an LGE-MRI-based virtual-heart arrhythmia ablation targeting (VAAT) methodology to non-invasively determine potential ablation targets for infarct-related VT. However, it is unknown whether VAAT’s predictions correspond with surface ECG predictions. Hypothesis: We hypothesized that the VAAT predicted VT circuits and potential ablation lesions would co-localize with ECG-based VT-exit predictions from a previously validated population-derived automated VT exit localization (PAVEL) system. Methods: We retrospectively enrolled 5 post-infarct patients who underwent LV endocardial VT ablation and had pre-procedural 2D LGE-MRIs. The PAVEL system based on a population-derived statistical method was used to localize VT-exit sites onto one of 238 triangles on the patient-specific virtual-heart LV endocardial surface using 8 independent ECG leads (I, II, V1-V6). The VAAT methodology incorporating patient-specific scar and infarct border zone distributions was used to identify potential VT circuits and find ablation lesions. Results: Eleven induced VTs were analyzed. Ten VT-exit sites were localized onto the patient-specific virtual-heart LV endocardial surface by the PAVEL system, and were used for the comparisons. One VT-exit site was too basal to be localized onto the virtual-heart geometry. The spatial resolution of the 10 predicted VT-exit sites was 13.8 ± 1.8 mm. VAAT-predicted VT circuits and ablation lesions correlated well with all 10 predicted VT-exit sites. Lastly, VAAT ablation lesions fell within the regions ablated clinically. Conclusions: The VAAT-predicted VT circuits and ablation lesions matched VT-exit sites predicted by the surface ECG-based PAVEL system. Combining these two complementary technologies may improve accuracy for non-invasively identifying optimal ablation targets to increase ablation efficacy.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".