The construction and application of an ultrasound and anatomical <scp>cross‐sectional</scp> database of structural malformations of the fetal heart
Bibliographic record
Abstract
OBJECTIVES: Establish a fetal heart anatomical cross-sectional database that correlates with screening transverse ultrasound images suggested by international professional organizations to detect congenital heart defects. METHODS: Fetuses with suspected congenital heart defects identified using the following cardiac image sequences obtained from transverse slices beginning from the upper abdomen and ending in the upper thorax were the subjects of this study: (1) four-chamber view, (2) left ventricular outflow tract view, (3) three-vessel right ventricular outflow tract view, and (4) the three-vessel tracheal view. A database of digital two-dimensional images of the transverse sweep was created for fetuses with confirmed congenital heart defects. In addition, using four-dimensional ultrasound spatial-temporal image correlation, selected transverse ultrasound images were acquired as part of the database. Ultrasound-detected congenital heart defects were confirmed postnatally from pathological specimens of the heart and lungs using a cross-sectional technique that mirrored the ultrasound images described above. When anatomical specimens were not available, prenatal ultrasound-detected congenital heart defects were confirmed using postnatal echocardiography and/or following surgery. RESULTS: The four screening views described in the Methods section identified 160 fetuses that comprised the database. Forty-five datasets consisted of both ultrasound and anatomical cross-sectional images. Thirteen percent (6/45) only had abnormalities of the four-chamber view (eg, endocardial cushion defects). Twenty-four percent (11/45) had abnormalities of the four-chamber view as well as right and left outflow tracts (eg, complex malformations). Of these, 10 of 11 had an abnormal tracheal view. Sixteen percent (7/45) had an abnormal four-chamber view and abnormal right outflow tract (eg, pulmonary stenosis). Thirty-three percent (15/45) had a normal four-chamber view but had abnormal right and left outflow tracts as well as an abnormal tracheal view (eg, tetralogy of Fallot, D-transposition of the great arteries). CONCLUSIONS: Combining both ultrasound and anatomical imaging may be of assistance in training imagers to recognize cardiovascular pathology when performing the screening examination of the fetal heart.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".