Keywords: Uterus, Machine Learning/Artificial Intelligence, Deep Learning, Cesarean scar pregnancy, Hemorrhage Risk, Dilation and Curettage
Motivation: Dilation and Curettage (D&C) is the standard treatment for cesarean scar pregnancy (CSP). However, existing quantitative approaches for preoperative assessment of intraoperative bleeding require manual feature selection, introducing uncertainty and hindering automation.
Goal(s): To develop an end-to-end deep-learning model for automatic and preoperative prediction of massive hemorrhage risk.
Approach: 109 CSP patients were included. A 3D VGG model was introduced based on high-resolution T2-weighted MR images.
Results: The prediction model achieved promising performance, with AUC, accuracy, specificity, and sensitivity at 0.9375, 0.9167, 0.9583, and 0.9167 on the test dataset, respectively.
Impact: This study presents an effective deep-learning solution for predicting hemorrhage in CSP patients, reducing manual intervention, and enhancing automation in clinical practice. The 3D VGG model's high accuracy and minimal preprocessing may streamline clinical decision-making and improve patient safety.
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