Fast Mask Recurrent Convolutional Neural Network for IoT-Based Maternal and Fetal Monitoring in High-Risk Pregnancies
Bibliographic record
Abstract
Ensuring the well-being of fetuses and their timely diagnosis for potential abnormalities is a critical aspect of healthcare. Early identification of intrauterine growth restriction can facilitate appropriate interventions and improve neonatal outcomes. This study presents a novel approach incorporating the Internet of Things (IoT) and Artificial Intelligence (AI) in the medical domain for the automatic detection of fetal abnormalities. IoT sensors were employed to gather maternal clinical data, including temperature, blood pressure, oxygen saturation levels, and fetal heart rate. A Fast Mask Recurrent Convolutional Neural Network (FMRCNN) was proposed to predict and accurately classify a range of conditions affecting pregnant women and their unborn children. The developed FMRCNN model learns, segments, and classifies fetal abdominal images to identify abnormalities. Additionally, a unified fetal abnormality prediction model was established to process and classify both fetal abdomen and brain ultrasound images. Comparative performance analysis was conducted using Convolutional Neural Networks (CNN), Random Forest (RF), and Support Vector Machine (SVM) algorithms. Evaluation metrics, such as F1score, accuracy, precision, recall, and sensitivity, were employed to assess the effectiveness of the proposed approach. The results indicate that the presented FMRCNN model holds promise for IoT-based maternal and fetal monitoring in high-risk pregnancies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".