Automated Deep Learning Detection Algorithms for Fetal Orientation and Placenta Previa
Bibliographic record
Abstract
Identifying the correct mode of fetal delivery is critical for ensuring the survival of both the mother and fetus, and it is influenced by fetal orientation and the presence of Placenta Previa (PP). To automate this process, we developed two deep-learning algorithms using Convolutional Neural Networks (CNNs) to classify fetal orientation and identify PP from two-dimensional (2D) Magnetic Resonance Imaging (MRI) slices. Our fetal orientation classifier, Fet-Net, achieved an average classification accuracy of 97.68% on 6120 MRI slices during a 5-fold cross-validation experiment. Our PP classifier, Previa-Net, performed with an average classification accuracy of 96.95% on 420 MRI slices across five random seeds. Both models outperformed state-of-the-art architectures such as VGG, ResNet, and Inception. By combining these two models, we can expedite the fetal exam reading for radiologists and determine the likelihood of surgical delivery based on fetal and placental positions, improving obstetric health care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".