Multimodal artificial intelligence for predicting postoperative cesarean scar diverticulum risk.

OBJECTIVES: To develop an artificial intelligence (AI) model to predict cesarean scar diverticulum (CSD) risk following cesarean scar pregnancy (CSP) for early clinical risk stratification. METHODS: A total of 120 CSP patients were retrospectively enrolled and randomly split into training (n=84) and test (n=36) cohorts. Data on clinical, laboratory, and ultrasonographic parameters, such as uterine scar muscle thickness, gestational sac diameter, and cesarean history, were gathered. A deep convolutional neural network (CNN) using a Faster R-CNN framework was trained to predict postoperative CSD, and feature importance analysis identified key predictors. RESULTS: Indicated significant differences in uterine scar muscle thickness, clinical classification, gestational sac diameter, and coagulation parameters between CSD and non-CSD groups (p<0.05). The AI-CNN model achieved an accuracy of 0.944, sensitivity of 0.917, and specificity of 0.958 in the test set. Key predictors included uterine scar muscle thickness of ≤0.2 cm, clinical classification type II-III, and a history of two or more cesarean sections. CONCLUSIONS: The AI-based CNN model provides accurate prediction of CSD risk after CSP. Identified preoperative indicators may guide clinical decision-making and targeted postoperative surveillance.