Modeling the wall thickness of abdominal aortic aneurysms in patients
Bibliographic record
Abstract
INTRODUCTION Aortic Abdominal Aneurysm (AAA) is defined as a focal dilation of the abdominal aorta greater than 3.0 cm. After an undetermined time after the onset of the pathology the wall weakens, eventually yielding rupture, which results in death in 90% of the cases. [1] AAAs are seldom detected at the early stage given absence of symptoms and the slow growth rate; however it has been shown that the stress on the wall is a better predictor of aneurysm rupture than simply measuring the diameter. [2] Computing physiological realistic wall stress requires: (i) an accurate 3D reconstruction of the AAA geometry, (ii) an appropriate constitutive law for the aneurysmal tissue and (iii) realistic loading and boundary conditions. METHODS Geometric reconstruction: To develop an anatomically realistic model of AAA a stack of CT images were imported into AAAVasc a MATLAB script [3] and segmented with an estimation of local distribution of AAA wall thickness. After obtaining the segmentation the mask was imported in ScanIP (Simpleware Ltd., Exeter, UK) and processed to obtain a preliminary grid of shells for FEM analysis. The mesh quality was then improved with a second mesh-processing package (Hypermesh; Altair Eng, Inc. Troy, MI). To account for the variable wall thickness the refined mesh was extruded into 3D hexahedral elements with an in house MATLAB code. Constitutive law: To study the mechanical behavior of the aneurismal wall, fresh human specimens obtained from the operating room where stretched until failure with uniaxial tensile testing system .[4] The Cauchy stress and stretch were calculated and fitted to estimate the material constitutive parameters. [5] Finally the combination of anatomically realistic model and the derived constitutive law allowed the evaluation of the effect of the variable wall thickness on the in-vivo stress states of patient-specific vascular geometries. RESULTS The model captures the macroscopic complexity of vascular tissue. In particular thick AAA wall showed a decrease in the mechanical properties as a result of wall weakening, possibly on account of inflammation. Including the wall thickness distribution and the changes in the mechanical properties redistributed the wall stress in a complex manner. Considering homogenous mechanical properties resulted in an underestimation of the wall stress. Figure 1. The design process in computing a physiological realistic wall stress model DISCUSSION AND CONCLUSIONS Combining patient specific models with the correct constitutive laws that account for the nonhomogeneous characteristics of the wall tissues can increase the reliability of the biomechanical stress predictions, which in turn improves the rupture risk assessment protocol for AAA.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".