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.