Characterizing the lung tissue mechanical properties using a micromechanical model of alveolar sac
Bibliographic record
Abstract
According to statistics, lung disease is among the leading causes of death worldwide. As such, many research groups are developing powerful tools for understanding, diagnosis and treatment of various lung diseases. Recently, biomechanical modeling has emerged as an effective tool for better understanding of human physiology, disease diagnosis and computer assisted medical intervention. Mechanical properties of lung tissue are important requirements for methods developed for lung disease diagnosis and medical intervention. As such, the main objective of this study is to develop an effective tool for estimating the mechanical properties of normal and pathological lung parenchyma tissue based on its microstructure. For this purpose, a micromechanical model of the lung tissue was developed using finite element (FE) method, and the model was demonstrated to have application in estimating the mechanical properties of lung alveolar wall. The proposed model was developed by assembling truncated octahedron tissue units resembling the alveoli. A compression test was simulated using finite element method on the created geometry and the hyper-elastic parameters of the alveoli wall were calculated using reported alveolar wall stress-strain data and an inverse optimization framework. Preliminary results indicate that the proposed model can be potentially used to reconstruct microstructural images of lung tissue using macro-scale tissue response for normal and different pathological conditions. Such images can be used for effective diagnosis of lung diseases such as Chronic Obstructive Pulmonary Disease (COPD).
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".