Characterization of aortic remodeling following Kawasaki disease: Toward a fully developed automatic biparametric model
Bibliographic record
Abstract
PURPOSE: Mechanical properties of the arteries are essential in assessing cardiovascular diseases. New imaging modalities that allow mapping strain, shear and elasticity distributions within the arterial wall are rapidly evolving. Very recently, our group introduced an iterative optical flow-based elastography method devoted to B-mode data. In this paper, the authors propose an adaptation of the optical flow method to investigate aortic remodeling following Kawasaki disease, an early childhood vasculitis. Namely, displacement and strain of the aortic wall are used to assess aortic stiffness in this human disease model. The authors also introduce a fully developed automatic method to support postprocessing data analysis. METHODS: The sequalae of Kawasaki disease on the ascending aorta were examined in children. The pathologic population (n = 4) was 15.00 ± 2.45 years old, while the healthy control population (n = 5) was 13.13 ± 0.18 years old. B-mode data were digitally recorded with commercially available cardiac echocardiography machines. RESULTS: Kawasaki disease had a very significant impact on the aortic stiffness. Indeed, pathologic subjects' aortic wall strain estimate was significantly lower compared to healthy controls (2.75% ± 0.56% versus 4.24% ± 0.65%, respectively; p < 0.001). Similarly, displacement of the aortic wall was also significantly lower compared to controls (p < 0.001). CONCLUSIONS: The potential of the optical flow-based method to quantify aortic wall remodeling in a human disease model was demonstrated. The authors now intend to extend this investigation to a larger pathologic cohort with various degrees of vasculitis severity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".