A deep learning late-fusion model using sagittal T2 MRI predicts postpartum hemorrhage risk with high accuracy.
Key Details
- 1Study involved 581 pregnant women with suspected placenta accreta who underwent placental MRI from May 2018 to June 2024.
- 2Models compared: 2D and 3D deep learning, radiomics, clinical, and ensemble fusion models.
- 3Best performance: late-fusion deep learning model (validation set AUC: 0.90, sensitivity: 92%, specificity: 91%).
- 4MRI remains crucial for evaluating placental abnormalities; AI enhances risk prediction.
- 5Earlier identification enables tailored delivery planning and preparedness for hemorrhage risk.
Why It Matters
Effective AI-driven risk prediction could enable earlier intervention and resource planning for postpartum hemorrhage, a leading cause of maternal mortality. This study demonstrates the potential for integrating advanced imaging AI into women's imaging protocols to directly impact patient outcomes.

Source
AuntMinnie
Related News

•AuntMinnie
AI for Breast Cancer Screening Not Cost-Effective, Study Finds
AI-assisted breast cancer screening showed minor clinical benefits over DBT alone but was not cost-effective at standard willingness-to-pay thresholds.

•AuntMinnie
AI Device Manufacturers Paid $39.7M for Radiology Between 2017–2023
AI/ML-enabled medical device firms paid $39.7 million for radiology devices from 2017–2023, raising transparency concerns.

•Radiology Business
AI-Generated Reports Cut Radiology Reading Times and Gain Acceptance
AI-generated reporting significantly reduces radiologists' reading times and increases report acceptability over time.