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

作者：Tianxin Qiu, Xinghe Zhou, Jun Zhou, Chunxia Lin, Shiling Jiang, Hui Cheng, Xinhao Wang, Qingshan You · 发表于：Frontiers in Physiology · 年份：2025 · DOI：10.3389/fphys.2025.1638788 · 被引用次数：2 · 研究领域：Neonatal and fetal brain pathology、ECG Monitoring and Analysis、Fetal and Pediatric Neurological Disorders

Introduction: Fetal heart monitoring (FHR) is a critical tool for assessing fetal health, but traditional methods rely on subjective physician interpretation, exhibiting significant variability that can lead to misdiagnosis and overtreatment. Artificial intelligence (AI) technology offers a novel approach to address this issue, yet existing research predominantly utilizes unimodal (FHR-only) data, failing to align with clinical guidelines emphasizing "bimodality analysis of fetal heart rate and uterine contractions (UC)." This study aims to develop a deep learning-based bimodal intelligent monitoring system to enhance the accuracy and clinical utility of fetal health assessment. Methods: The research team constructed the first fetal heart-contraction bimodal clinical dataset for Chinese pregnant women (n = 326). Based on the DenseNet121 architecture, a selective attention mechanism (SK module) was introduced, proposing the DenseNet121-SK model. Standardized FHR and UC signals were extracted using image processing techniques. Dense connections and the SK module dynamically fused multi-scale features (e.g., transient fluctuations and contraction cycle associations). The model employed lightweight design during training to enhance physician usability. Results: (1) Dual-modality input significantly outperformed single-modality input, achieving a classification AUC of 0.944 (vs. 0.812 for single-modality), validating the clinical value of multi-parameter collaborative interpretati...