Association between deep learning radiomics based on placental MRI and preeclampsia with fetal growth restriction: A multicenter study
Bibliographic record
Abstract
PURPOSE: Preeclampsia (PE) is associated with placental insufficiency and could lead to adverse pregnancy outcomes. The study aimed to develop a placental T2-weighted image-based automatic quantitative model for the identification of PE pregnancies and disease severity. METHODS: Between July 2013 and September 2022, the retrospective multicenter study featured 420 pregnant women, including 140 cases of PE and 280 cases of normotensive pregnancies. The semi-supervised approach was used to gain an automatic segmentation for placental MRI. The radiomics, deep learning, and deep learning radiomics (DLR) models were built. RESULTS: In PE pregnancies, 65 (46.4 %) fetuses developed PE with fetal growth restriction (FGR), and 75 (53.6 %) cases were PE without FGR. The Dice of semi-supervised placental segmentation was 0.917. The AUCs of the DLR signature for discriminating PE pregnancies from normotensive pregnancies were 0.839 (95 % CI: 0.793-0.886), 0.858 (95 % CI: 0.742-0.974), 0.888 (95 % CI: 0.783-0.992), and 0.843 (95 % CI: 0.731-1.000) in the training, test, internal validation, and external validation sets, respectively. This DLR analysis model performed well in discriminating between PE with FGR and normotensive pregnancies (AUC = 0.918, 95 % CI: 0.879-0.957) and PE without FGR (AUC = 0.742, 95 % CI: 0659-0.824). CONCLUSION: The automatic radiomics analysis has been developed to identify PE pregnancies by determining DLR features on placental T2-weighted images, and to predict FGR exposed to PE.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".