Abstract:
Objective: Cardiotocography (CTG) is commonly used to monitor fetal heart rate (FHR) and assess fetal well-being during labor. However, its effectiveness in reducing adverse outcomes remains limited due to low sensitivity and high false-positive rates. This study aims to develop an interpretable deep learning model that fuses FHR time series with tabular clinical features to improve prediction of fetal compromise (umbilical artery pH < 7.05). Methods: We introduce Fusion ResNet, a novel architecture combining residual convolutional networks for FHR signal processing with a parallel neural network for tabular features. The model was trained and internally validated on a private dataset of 9,887 FHR recordings. External validation was performed on the open-access CTU-UHB dataset comprising 552 recordings. Model interpretability was evaluated using Shapley Additive Explanations (SHAP) and Gradient-Weighted Class Activation Mapping (Grad-CAM). Results: Fusion ResNet achieved a mean area under the ROC curve (AUC) of 0.77 during internal cross-validation and a state-of-the-art AUC of 0.84 on the CTU-UHB dataset, outperforming existing deep learning approaches. SHAP analysis identified key clinical features contributing to predictions, while Grad-CAM highlighted salient FHR patterns linked to fetal compromise. Conclusion: The proposed model enhances predictive accuracy while providing clinically meaningful explanations, enabling more transparent and reliable CTG interpretation. Significance: This work demonstrates the potential of interpretable deep learning to improve fetal monitoring by integrating multimodal data, supporting timely and informed decision-making in obstetric care.
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