Design of coronary artery phantom using polyvinyl alcohol cryogel for optical coherence tomography imaging in Kawasaki disease
Bibliographic record
Abstract
Kawasaki disease, a childhood pathology, is marked by the potential for coronary artery complications, which can lead to the dilation or inflammation of the blood vessel wall if left untreated. Intravascular Optical coherence tomography (IVOCT) was introduced for intravascular imaging of coronary arteries to provide valuable navigation guidance information to cardiologists. It requires a skilled operator, and the acquisition protocol is complex. The goal of this study is to present a framework to reproduce patient specific coronary OCT phantoms using polyvinyl alcohol cryogel (PVA-c), which can be used for training cardiologists and for better understanding of the OCT image formation process. This innovative approach enables us to produce phantoms with both mechanical and optical properties very similar to human tissue. To produce these phantoms, we design and print in 3D modular cylindrical molds from real OCT arterial images. A mixture of PVA is poured into the molds and submitted to three thaw and freeze cycles to create soft tissue that represent coronary arteries affected by Kawasaki disease. Once the phantoms have been created, OCT pull-back sequences are acquired and compared to the original images. We acknowledged that our PVA-c phantoms reproduces morphological shape and visual appearance on OCT very similar to human tissue. This holds true even when applied to extremely small morphologies.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".