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Abstract #0169

MRI-based Deep Radiomics Ensemble Learning for Predicting Postpartum Hemorrhage in Patients with Suspected Placenta Accreta Spectrum

Qi Zhang1, Haijie Wang2, Xiaoyun Liang2, Wenyi Yue3, Ruxue Han3, He Zhang4, and Guang Yang1
1Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, ShangHai, China, 2Institute of Research and Clinical Innovations, Neusoft Medical Systems Co., Ltd, Beijing, China, 3Beijing Chaoyang Hospital, Capital Medical University, Beijing, China, 4Department of Radiology, Obstetrics and Gynecology Hospital,, Fudan University, ShangHai, China

Synopsis

Keywords: Placenta, Placenta, Postpartum hemorrhage,Placenta accreta spectrum

Motivation: Accurately predicting postpartum hemorrhage (PPH) risk in patients with suspected placenta accreta spectrum (PAS) disorders remains a clinical challenge.

Goal(s): To develop an ensemble model integrating deep learning (DL) and radiomics to predict PPH risk in suspected PAS patients.

Approach: 538 patients from multiple medical centers were recruited retrospectively. Using T2WI, a multitask DL model was trained for placenta segmentation, PPH classification and estimated blood loss (EBL) regression. Radiomics features were extracted and integrated with the DL to enhance predictive power.

Results: The ensemble model demonstrated satisfying performance, significantly enhancing PPH risk assessment and EBL prediction in both internal and external testing sets.

Impact: This study demonstrates an automated pipeline integrating deep learning and radiomics to predict postpartum hemorrhage in high-risk pregnancies using MRI. It enhances prenatal risk assessment, potentially improving clinical outcomes by enabling timely intervention and better resource allocation in obstetric care.

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