Quantitative ultrasound characterization of the human placenta for detection of placenta-mediated pregnancy complications
Bibliographic record
Abstract
Quantitative Ultrasound in Placenta (QUS-P) measures the acoustic properties of the underlying tissue and therefore can be used to detect structural changes associated with placenta-mediated diseases in utero. To develop a real-time, non-invasive clinical tool using QUS-P, we first conducted an ex-vivo study on post-delivery human placentas (n = 47), of which 25 were from pregnancies affected by pre-eclampsia (PE) and/or small-for-gestational age (SGA). Ultrasound radio-frequency data were analyzed to estimate QUS-P parameters: attenuation coefficient, backscatter coefficient and effective scatterer diameter. A logistic regression model developed using these QUS-P parameters achieved high discrimination (AUROC: 0.89 (95% CI: 0.78–0.98)) and calibration (slope: 0.93 and intercept 0.003) for PE/SGA detection. Building on these findings, we conducted STIMULUS, an in utero study involving pregnant participants (34–36 weeks gestation). Preliminary analysis (n = 184) identified 29 (15.8%) neonates with perinatal hypoxia, 5 of which showed maternal/fetal vascular malperfusion. Logistic regression using QUS-P parameters predicted hypoxia with an AUROC of 0.70 (95% CI: 0.58–0.81) and the sub-category of placenta-mediated hypoxia with an AUROC of 0.98 (95% CI: 0.88–1.0). The findings demonstrate that QUS-P holds potential for integration into prenatal care as an early, non-invasive tool for predicting placenta-mediated diseases before clinical onset.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".