Characterization of the Biomechanical Properties of the Lower Esophagus for Surgical Simulation
Bibliographic record
Abstract
In this work a method to characterize soft tissue properties for mechanical modeling is presented. Attention is especially focused on developing a model of the lower esophagus to be used in a surgical simulation, which shows a promise as a training method for medical personnel. The viscoelastic properties of the lower esophageal junction are characterized using data from animal experiments and an inverse FE parameter estimation algorithm. Utilizing the assumptions of quasilinear- viscoelastic theory, the viscoelastic and hyperelastic material parameters are estimated to provide a physically based simulation of tissue deformations in real time. To calibrate the parameters to the experimental results, a three dimensional FE model that simulates the forces at the indenter and an optimization program that updates new parameters and runs the simulation iteratively are developed. It was possible to reduce the time and computation resources by decoupling the viscoelastic part and elastic part in a tissue model. The comparison of the simulation and the experimental behavior of pig esophagus are presented to provide validity to the tissue model using the proposed approach.
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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 | 0.000 | 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) | 0.002 | 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".