Automated Prediction of Subsequent Miscarriage Risk in Pregnant Women by Early First-Trimester Ultrasound...

Predicting the risk of subsequent miscarriage during the first trimester is crucial for optimizing ultrasound surveillance and alleviating psychological distress for pregnant women. This study aims to develop a multi-input convolutional neural network (CNN) that integrates early gestational ultrasound images with clinical measurements to assess miscarriage risk, while providing interpretable visual explanations to support clinical decision-making. Utilizing a retrospective dataset of singleton pregnancies at 6-8 weeks of gestation, we extracted gestational sac regions through three distinct segmentation strategies and trained a CNN-based classifier to differentiate between normal and miscarriage outcomes. The predictive performance was evaluated using accuracy, precision, recall, f1-score, and the area under the receiver operating characteristic curve (AUC). The results indicated that the multi-input CNN outperformed single-input models. Additionally, an edge-expanded segmentation strategy demonstrated superior performance compared to both precise segmentation and rectangular cropping. By incorporating Gradient-weighted Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP), we identified subchorionic regions as significant biomarkers and clarified the differences in attention to features among different models, thereby enhancing the interpretability and clinical applicability of the model for early pregnancy risk assessment.