Computational simulations of the aortic valve validated by imaging data: evaluation of valve-sparing techniques☆
Bibliographic record
Abstract
The goal of this study has been to develop a numerical model of the aortic valve, to validate it with in-vivo data and to computationally evaluate the effect of two types of aortic valve-sparing reconstructions on valve dynamics and hemodynamics. A model of the native aortic valve and two models of the valve after surgical reconstruction (reimplantation with a straight conduit and remodeling with a shaped conduit) were created. These models were transferred to a finite element analysis software where the interaction between valve structures and blood was taken into account in a dynamic manner. Leaflet and blood dynamics, as well as tissue compliance and stresses were evaluated. Leaflet dynamics and blood velocities were also assessed by magnetic resonance imaging in 15 healthy volunteers. Computational results in the native valve model correlated closely with the in-vivo imaging data. The creation of neo-sinuses was shown to restore leaflet opening and closing dynamics. Loss of compliance at the commissures led to altered stress distribution patterns. Preservation of sinus geometry was an important factor in end systolic vortex formation. This is the first study to have incorporated the effect of blood flow in the numerical evaluation of aortic reconstructions using a computational model validated by in-vivo data. Differences in valve dynamics after surgical reconstruction reported in this computational study match trends previously reported in other in-vivo studies. Numerical models such as this one can serve as increasingly sophisticated tools in the study of aortic valve pathologies and in the optimization of new surgical reconstruction techniques.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".