Development and external validation of deep learning models for spontaneous preterm birth prediction from mid-trimester cervical ultrasound
This study developed and externally validated deep learning and multimodal models for predicting spontaneous preterm birth using mid-trimester cervical ultrasound, finding that while internal performance was modest, external validation yielded poor results likely due to the heterogeneity of preterm birth, suggesting a need for subtype-specific modeling and additional biomarkers.