Abstract 9642: Non-Invasive Magnetic Resonance Based Three-Dimensional Voltage Maps for Electrophysiology Procedure Guidance
Bibliographic record
Abstract
Introduction: The association of scar on late-gadolinium enhancement cardiac magnetic resonance (LGE-CMR) with electrograms on electroanatomic map (EAM) has been investigated. We sought to quantify these associations to enable the creation of non-invasive three dimensional voltage maps (3D-VMs) based on LGE-CMR. We then tested the accuracy of the non-invasive 3D-VMs. Methods: LGE-CMR was performed in 17 patients with ischemic cardiomyopathy before ventricular tachycardia (VT) ablation. Left ventricular wall thickness (LVWT) and scar thickness (ST) were measured in each of 20 sectors per LGE-CMR short axis plane. In the first series of 13 patients (training set), EAM points were registered to the corresponding LGE-CMR images. Multivariate linear regression analysis (MLRA) was performed to determine significant independent variables and coefficients that predict local bipolar voltage. In the remaining patients (test set), non-invasive 3D-VMs were prospectively created with the regression equations by MLRA. Invasive local bipolar voltages were then compared with the estimated bipolar voltage based on non-invasive 3D-VMs. Results: A total of 1293 EAM points were analyzed. MLRA revealed independent associations between local bipolar voltage and LVWT, ST and scar location (P<0.001, respectively). Prospective non-invasive 3D-VMs were then created with custom software. There was no significant difference in mean bipolar voltage on EAMs and non-invasive 3D-VMs created for the test set (1.7±1.2 vs. 1.6±1.7 mV,P=0.17). Linear regression analysis revealed a significant association between bipolar voltages on invasive EAM and noninvasive 3D-VMs (P<0.001, R=0.79). Conclusions: The independent associations of local bipolar voltage with LVWT, ST on LGE-CMR enable the creation of accurate non-invasive 3D-VMs based on LGE-CMR. This novel methodology may improve the safety and efficacy of catheter ablation in patients with ischemic scar-related VT.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.008 | 0.003 |
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".