Abstract 153: Automated Angiographic Assessment Of Coronary Artery Vasomotion In Kawasaki Disease Patients
Bibliographic record
Abstract
Background: Mechanical properties of coronary arteries (CA) hold clues to vascular health and viability. Traditionally assessed with intracoronary imaging, we present an angiography-based system to assess CA vasomotion using automatic vessel segmentation and spatio-temporal tracking. Elastic moduli computed from dynamic CA calibers are compared between non-KD patients (CTL), KD patients with no CA aneurysms (KDAN-), and those with aneurysms (KDAN+). Methods: Proximal CA angiograms are automatically segmented and tracked over a cardiac cycle. CA centerline is extracted and the mean caliber is computed from diameters along its length. The resulting caliber variation reflects the CA vasomotion (Figure 1a). We then calculated the Vasomotion Standard Deviation (VSD) and CA recoil with the mean constriction velocity (MCV). Finally, Elastic Pressure moduli were computed using trans-myocardium pressure gradients. Results: We analyzed 51 left CA segments from 23 patients (5 CTL, 5 KDAN-, 13 KDAN+). Data are mean ± SD normalized pixels (npx). VSD was significantly reduced ( p <0.01) in KDAN+ (0.25±0.05) and KDAN- (0.27±0.04) vs CTL (0.38±0.07 npx). Coronary recoil was significantly reduced (p<0.05) in KDAN+ vs CTL, with MCV 3.50±0.67 vs 4.59±1.94 npx/sec. Pressure-dependent stiffness characteristics were equally atypical (Figure 1b). Conclusion: The proposed angiography-based stiffness assessment system shows abnormal CA vascular physiology in our cohort of KD patients. These results concur with previous invasive studies. The potential usability of this system for vascular health assessment could be applied to previously recorded CA angiograms for risk stratification.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".