In the global healthcare landscape, a staggering 287,000 maternal deaths, 2.4 million neonatal deaths, and 1.9 million stillbirths annually cast a long shadow, with the intrapartum period being the stage for a significant portion, approximately 45%, of these tragedies, predominantly in low- and middle-income countries. Quality intrapartum care is thus the linchpin for reducing global maternal and neonatal morbidity and mortality.

We employed a hybrid model to predict the delivery mode, integrating discrete data (EHR and US) with continuous signal data (cCTG, which provides a continuous graphic record of fetal heart rate (FHR) and uterine contractions (UCs)). The hybrid model combines a convolutional neural network (CNN) and bi-directional long short-term memory (BiLSTM) to capture complex nonlinear spatial and temporal features of the cCTG, while features from EHR and US were digitized and normalized. These data types were then fused to perform classification. 

The designed model achieves a cross-validation accuracy of 93.33%, an F1-score of 86.26%, an area under the receiver operating characteristic curve of 97.10%, and a Brier Score of 6.67%. Importantly, while cCTG and EHRs are crucial for labor management, the integration of US imaging data significantly enhances prediction accuracy.

Bai J, Kang X, Wang W, et al. A multimodal model in the prediction of the delivery mode using data from a digital twin-empowered labor monitoring system. DIGITAL HEALTH. 2024;10. doi:10.1177/20552076241304934