Leaflet geometry extraction and parametric representation of a pericardial artificial heart valve
Bibliographic record
Abstract
Reverse engineering technology was used to reconstruct the complex leaflet geometry of a commercial pericardial valve in our study. Results show that the three-dimensional computer-aided design model of the leaflet surface can be rendered by fitting the surface either to cloud points or by a group of B-splines fitted to a set of cloud points that had been obtained by the process of laser-scanning digitizing. However, an acceptable smooth surface is usually not guaranteed and additional manipulation is required. An alternative method is introduced in this paper, which involves the fitting of an equation to the leaflet geometry to create a smooth surface. The geometrical profile of a pericardial artificial heart valve was scanned using a laser digitizing system. The leaflet profile is represented as a set of cloud points. A quadric surface is fitted to a set of unique points, which were located on the set of cloud points. A mathematical equation is obtained by solving a least-squares fit. The geometry of the fitted leaflet surface has been proven to be closely represented by an elliptical hyperboloid. The quadratic equations of the leaflet curvatures, calculated along both the circumferential and the radial directions, resulted in simple hyperbolic curvatures. The advantages of using elliptical hyperboloid geometry for the leaflet surface are discussed and compared with other types of conicoid geometries. The concepts of parametric representation of the leaflet geometry and parametric design for leaflets are discussed. A smooth surface without inflection points and with an adjustable surface area suitable for a series of stent sizes with incremented diameters is created by this method of a single parametric design. Finally, a generic method to apply the geometry extraction and parametric representation to most pericardial heart valve prostheses was discussed. The application to valves with natural shape was introduced, challenges were identified, and a technical solution was proposed.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".