Fetal Assessment Suite (FetAS): a web-based platform for automatic fetal MRI analysis using AI
Bibliographic record
Abstract
Fetal MRI provides high-resolution, three-dimensional imaging with superior soft tissue contrast, offering critical diagnostic insights that complement ultrasound, particularly in cases of suspected abnormalities. However, its interpretation remains labour-intensive and highly dependent on subspecialized radiological expertise, which limits accessibility and contributes to diagnostic delays. To address these challenges, we present the Fetal Assessment Suite (FetAS), a secure, web-based platform designed to streamline and standardize fetal MRI analysis. FetAS automates key tasks, including artifact detection, motion correction, segmentation of the fetal body, amniotic fluid, and placenta, as well as classification of fetal and placental position and orientation, using AI models developed to reduce reliance on expert radiologists. By consolidating these capabilities into a single, user-friendly interface, FetAS reduces the cognitive and time burden of interpretation, supports timely clinical decision-making, and promotes equitable access to care in regions without local expertise. In addition, FetAS enables research through standardized pipelines, facilitates data sharing, and provides a foundation for future advancements in maternal-fetal imaging. Collectively, these features establish FetAS as both a diagnostic tool and a platform for expanding high-quality prenatal care across diverse healthcare settings.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.012 |
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".