Preliminary Experience Using Motion Compensated CINE Magnetic Resonance Imaging to Visualise Fetal Congenital Heart Disease
Bibliographic record
Abstract
BACKGROUND: Recent advances in cardiovascular magnetic resonance (CMR) imaging have facilitated CINE imaging of the fetal heart. In this work, a preliminary investigation of the utility of multislice CINE CMR for assessing fetal congenital heart disease is performed and compared with echocardiography. METHODS AND RESULTS: Multislice CINE CMR and echocardiography images were acquired in 25 pregnant women wherein the fetus had a suspected congenital heart defect based on routine obstetric ultrasound. Pathognomonic images were identified for each subject for qualitative comparison of CMR and echocardiography. Quantitative comparison of CMR and echocardiography was then performed by 2 reviewers using a binary scoring of 9 fetal cardiac anatomic features (identifiable/not-identifiable). Pathognomonic images demonstrated the ability of CMR to visualize a variety of congenital heart defects. Overall CMR was able to identify the majority of the 9 assessed fetal cardiac anatomic features (reviewer 1, 7.1±2.1; reviewer 2, 6.7±2.3). Although both reviewers identified more anatomic features with echocardiography (reviewer 1, 7.8±2.3; reviewer 2, 7.5±2.4; P=0.01), combining information from both modalities enabled identification of additional anatomic features across subjects (reviewer 1, 8.4±1.3; reviewer 2, 8.4±1.2). The primary limiting factor for CMR was inadequate coverage of the fetal cardiac anatomy or noncontiguous slices because of gross fetal movement. CONCLUSIONS: CINE CMR enables visualization of fetal congenital heart disease. This work demonstrates the potential of CMR for diagnosing congenital heart disease in utero in conjunction with echocardiography during late gestation.
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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 | 0.031 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".