Towards Durability Estimation of Bioprosthetic Heart Valves Via Motion Symmetry Analysis
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Bibliographic record
Abstract
This paper addresses bioprosthetic heart valve (BHV) durability estimation via computer vision (CV)-based analyses of the visual symmetry of valve leaflet motion. BHVs are routinely implanted in patients suffering from valvular heart diseases. Valve designs are rigorously tested using cardiovascular equipment, but once implanted, more than 50% of BHVs encounter a structural failure within 15 years. We investigate the correlation between the visual dynamic symmetry of BHV leaflets and the functional symmetry of the valves. We hypothesize that an asymmetry in the valve leaflet motion will generate an asymmetry in the flow patterns, resulting in added local stress and forces on some of the leaflets, which can accelerate the failure of the valve. We propose two different pair-wise leaflet symmetry scores based on the diagonals of orthogonal projection matrices (DOPM) and on dynamic time warping (DTW), computed from videos recorded during pulsatile flow tests. We compare the symmetry score profiles with those of fluid dynamic parameters (velocity and vorticity values) at the leaflet borders, obtained from valve-specific numerical simulations. Experiments on four cases that include three different tricuspid BHVs yielded promising results, with the DTW scores showing a good coherence with respect to the simulations. With a link between visual and functional symmetries established, this approach paves the way towards BHV durability estimation using CV techniques.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it