The Virtual Transcatheter Aortic Valve Replacement (VTAVR) framework predicts optimal device landing zones tailored to patient-specific anatomy
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Bibliographic record
Abstract
VTAVR, a novel simulation for Transcatheter Aortic Valve Replacement (TAVR), optimizes device placement using routine patient-specific CT angiography data. It integrates image processing, geometric reconstruction, and centerline estimation for accurate valve deployment. The framework employs a kinematic simulator to optimize valve performance by adjusting parameters like expansion area, anchoring depth, and implantation height, aiming to reduce complications such as paravalvular leaks (PVL) and left bundle branch block (LBBB). In this retrospective study (N = 40; pre and post TAVR), VTAVR demonstrated high fidelity with average Surface Error of pre CT simulated device versus in-vivo post CT stent frame (L2 Norm) Median: 0.633 mm; IQR= [0.216–1.37 mm]. Median post-TAVR CT device diameters were 24.4 mm [22.0–25.9 mm] at the outflow, 24.4 mm [22.5–26.0 mm] at the midflow, and 24.9 mm [22.9–26.7 mm] at the inflow, showing no significant differences compared to VTAVR simulations (p < 0.001). Median implantation height was 8.1 mm [6.9–10.4 mm] vs 7.2 mm [6.7–8 mm], with VTAVR predicting similar heights (p < 0.05). Additionally, VTAVR accurately predicted the area cover index, with a median of 101.4% [91.9–105.3%] closely matching post-TAVR CT (p < 0.01). The system provides assessments of peri-procedural risk factors by quantifying geometrical “safety” margins, aiming to minimize common complications such as improper implantation depth and over-expansion. VTAVR’s simulation of various deployment scenarios allows clinicians to foresee and address potential complications effectively, marking a significant advance in personalized cardiac interventions through virtual, non-invasive pre-procedural optimization.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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 it