Towards a Biomechanical-Based Method for Assessing Myocardial Tissue Viability
Bibliographic record
Abstract
This work presents the first steps towards the development and implementation of a novel 3D biomechanical-based method for assessing the viability of myocardial tissue, with particular interest for its application in myocardial infarction (MI) diagnosis. This assessment technique quantifies the myocardial contraction forces developed within the ventricular myofibrils in response to the electrophysiological stimulus. In this manuscript we provide a 3D finite element (FE) formulation of a contraction force reconstruction algorithm based on an inverse problem solution of linear elasticity, along with its implementation using clinical data. This algorithm has been applied to patient-specific models obtained by extracting anatomical features from high-resolution, high-contrast magnetic resonance (MR) cardiac images. The input consists of motion information extracted by nonrigid registration of the mid-diastole reference image to the remaining images of the 4D data set, acquired using ECG-gating throughout the cardiac cycle. The result consists of a display-map of the contraction force distribution superimposed on the anatomical ventricle model, which allows the clinician to identify regions of low contractility in the myocardium.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".