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
Study: Computer Vision Models Best LLMs in Chest CT Breast Abnormality Detection
Computer vision models (CVMs) surpass large language models (LLMs) in accurately labeling incidental breast abnormalities on chest CT scans.
•Radiology Business
Private Equity Backs AIRS Medical to Expand MRI AI Globally
TA Associates is investing in AIRS Medical to accelerate its global expansion of AI-powered MRI efficiency solutions.
•AuntMinnie
Deep Learning Models Rival Radiologists for Pancreatic Cancer Detection on CT
Deep-learning models achieved comparable or superior accuracy to experienced radiologists in detecting pancreatic cancer on CT scans, especially for small tumors.