Noninvasive assessment of myocardial perfusion using ultrafast ultrasound: clinical study for congenital heart disease
Bibliographic record
Abstract
Abstract Aims Myocardial perfusion impacts cardiac function following surgical repair of critical congenital heart disease (CCHD). Temporal variation assessment of myocardial blood volume throughout the cardiac cycle can be a surrogate for perfusion. Ultrafast power Doppler (UPD) is an ultrasound imaging technique capable of noninvasively quantifying myocardial blood volume changes. The objective of this study is to demonstrate the feasibility of perioperative transthoracic UPD assessment and to determine if UPD reflects physiologic changes in myocardial perfusion. Methods and results Five neonatal transposition of the great arteries (TGA) undergoing arterial switch operation (ASO), five hypoplastic left heart syndrome (HLHS) undergoing Stage 1 palliation (S1P), and five age/weight-matched controls were prospectively recruited. Transthoracic UPD acquisitions were performed before/after operations. Segmental perfusion in right/left ventricles (RV/LV) was assessed. The controls’ myocardial perfusion patterns are visually similar to published human references for both ventricles. Systolic/diastolic myocardial perfusion differences were modified by ASO in the RV (P = 0.03) but not for LV (P = 0.99). For HLHS patients, no difference after S1P was observed in either the RV (P = 0.16) nor the LV (P = 0.51). Conclusion For TGA patients, the perfusion profile of the myocardium seems to be directly influenced by the intracavitary pressure (directly driving coronary perfusion pressure), namely if it was the systemic or sub-pulmonary ventricle. Our data suggests that UPD could noninvasively quantify myocardial perfusion variation. Myocardial perfusion patterns change in CCHD according to their haemodynamic and surgical status. Correlation with clinical outcomes requires further study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".