A Feature-Based Morphing Methodology for Computationally Modeled Biological Structures Applied to Left Atrial Fiber Directions
Bibliographic record
Abstract
To properly simulate the behavior of biological structures through computer modeling, there exists a need to describe parameters that vary locally. These parameters can be obtained either from literature or from experimental data and they are often assigned to regions in the model as lumped values. Furthermore, parameter values may be obtained on a representative case and may not be available for each specific modeled organ. We describe a semiautomated technique to assign detailed maps of local tissue properties to a computational model of a biological structure. We applied the method to the left atrium of the heart. The orientation of myocytes in the tissue as obtained from histologic analysis was transferred to the 3D model of a porcine left atrium. Finite element method (FEM) dynamic simulations were performed by using an isotropic, neo-Hookean, constitutive model first, then adding an anisotropic, cardiomyocyte oriented, Fung-type component. Results showed higher stresses for the anisotropic material model corresponding to lower stretches in the cardiomyocyte directions. The same methodology can be applied to transfer any map of parameters onto a discretized finite element model.
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How this classification was reachedexpand
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".