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This is a Quantitative Study study.

September 30, 2025Frontiers in PhysiologyOpen Access

AI-augmented prenatal care: a dual-modal fetal health assessment system integrating cardiotocography and uterine contraction synergy

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Deep learning model improves fetal health accuracy in pregnant women by integrating fetal heart rate and uterine contractions.

Dual-modality input achieved a classification AUC of 0.944, exceeding unimodal performance significantly.
The SK module demonstrated 95.88% accuracy with 100% recall for abnormal fetal cases, enhancing clinical decision-making.
Lightweight AI design reduces subjective interpretation variability, making it suitable for resource-constrained healthcare settings.
Future developments aim to optimize generalization through multicenter validation and large language model integration.

Qiu et al. (2025) studied this question.

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September 30, 2025Frontiers in PhysiologyOpen Access

View Full Paper

Ask AI

Bookmark

Authors

Deep learning model improves fetal health accuracy in pregnant women by integrating fetal heart rate and uterine contractions.

Dual-modality input achieved a classification AUC of 0.944, exceeding unimodal performance significantly.
The SK module demonstrated 95.88% accuracy with 100% recall for abnormal fetal cases, enhancing clinical decision-making.
Lightweight AI design reduces subjective interpretation variability, making it suitable for resource-constrained healthcare settings.
Future developments aim to optimize generalization through multicenter validation and large language model integration.

Qiu et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark