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Multimodal artificial intelligence for predicting postoperative cesarean scar diverticulum risk
Jiannan Wang1, Quan Liu1, Yuanyuan Wang1
1Department of Obstetrics, Hefei First People's Hospital (South Campus), Hefei, China.
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.