Current and future role of fetal cardiovascular MRI in the setting of fetal cardiac interventions
Bibliographic record
Abstract
Over recent years, technical developments resulting in the feasibility of fetal cardiovascular magnetic resonance (CMR) have provided a new diagnostic tool for studying the human fetal heart and circulation. During the same period, we have witnessed the arrival of several minimally invasive fetal cardiac interventions (FCI) as a possible form of treatment in selected congenital heart diseases (CHDs). The role of fetal CMR in the planning and monitoring of FCI is not yet clear. Indeed, high-quality fetal CMR is not available or routinely offered at most centers caring for patients with prenatally detected CHD. However, in theory, fetal CMR could have much to offer in the setting of FCI by providing complementary anatomic and physiologic information relating to the specific intervention under consideration. Similarly, fetal CMR may be useful as an alternative imaging modality when ultrasound is hampered by technical limitations, for example, in the setting of oligohydramnios and in late gestation. In this review, we summarize current experience of the use of fetal CMR in the diagnosis and monitoring of fetuses with cardiopathies in the setting of a range of invasive in utero cardiac and vascular interventions and medical treatments and speculate about future directions for this versatile imaging medium.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".