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
•Radiology Business
Framework Assesses Real-World Financial Impact of Radiology AI Adoption
A new analysis presents a financial calculator for objectively assessing the return on investment (ROI) of implementing radiology AI solutions.
•Radiology Business
AI Technique Unveils Previously Hidden MS Gray Matter Lesions on MRI
Researchers developed an AI-enhanced method to detect previously invisible gray matter lesions in multiple sclerosis using MRI.
•Radiology Business
Majority of Patients Want Disclosure When AI Used in Imaging
A new survey finds that nearly all patients want to be informed when AI is utilized in medical imaging interpretation.