Increasing the Detection Rate of Congenital Heart Disease During Routine Obstetric Screening Using Cine Loop Sweeps
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to demonstrate an increase in the detection rate of fetal cardiac defects using 2 cine loop sweeps. METHODS: Image reviewers examined a series of 93 cases randomly sorted, including 79 studies with normal findings and 14 studies with abnormal findings. All of the images were assessed by 5 standard criteria. Cases were classified as normal, abnormal, or indeterminate. Reviewers using the conventional approach reviewed 3 still images: the 4-chamber, left ventricular outflow tract, and right ventricular outflow tract views. Reviewers using the cine loop sweeps viewed 2 grayscale sweeps through the fetal heart in real time. The image sequences were reviewed independently by 2 experts, 3 nonexperts, and 2 sonographers blinded to each others' results. RESULTS: The cine loop sweeps had an increased detection rate of 38% for the nonexperts and 36% for the experts compared with the conventional approach. The cine loop sweeps allowed identification of all cardiac defects by at least 2 of the 7 reviewers; the percentage of cases with false-positive findings was 3.9%. With the conventional approach, 2 defects went undetected by all reviewers, and 4 defects were found by only 1 reviewer; the percentage of cases with false-positive findings was 5.4%. CONCLUSIONS: The use of cine loop sweeps has the potential to increase the detection of fetal cardiac defects without increasing the rate of false-positive findings or increasing the interpretation and decision-making times.
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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 | 0.015 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".