Imaging-based biomechanical parameters for assessing risk of aortic dissection and rupture in thoracic aortic aneurysms
Bibliographic record
Abstract
OBJECTIVES: Imaging-based methods of measuring aortic biomechanics may provide superior and a more personalized in vivo risk assessment of patients with thoracic aortic aneurysms compared to traditional aortic size criteria such as maximal aortic diameter. We aim to summarize the data on in vivo imaging techniques for evaluation of aortic biomechanics. METHODS: A thorough search of literature was conducted in MEDLINE, EMBASE and Google Scholar for evidence of various imaging-based biomechanics techniques. All imaging modalities were included. Data involving preclinical/animal models or exclusively focussed on abdominal aortic aneurysms were excluded. RESULTS: The various imaging-based biomechanical parameters can be divided into categories of increasing complexity: strain-based, stiffness-based and computational modelling-derived. Strain-based and stiffness-based parameters are more simply calculated and can be derived using multiple imaging modalities. Initial studies are promising towards linking these parameters with clinically relevant end-points, including aortic dissection, though work is required for standardization. Computationally derived parameters provide detail of stress exerted on the aortic wall with great spatial resolution. However, they are highly dependent on the assumptions applied to the models, such as material properties of the aortic wall. CONCLUSIONS: Imaging-based aortic biomechanics represent a major technical advancement for personalized in vivo risk stratification of patients with ascending thoracic aortic aneurysm. The next steps in clinical translation require large-scale validation of these markers towards predicting aortic dissections and comparison against the gold standard ex vivo aortic biomechanics as well as development of a user-friendly, low-cost algorithm that can be widely adopted.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".