Ultrasound AI's study validates advanced AI for predicting delivery timing using standard ultrasound images.
Key Details
- 1Ultrasound AI, in collaboration with University of Kentucky, published results in The Journal of Maternal-Fetal & Neonatal Medicine.
- 2The AI predicts time to delivery using only standard ultrasound images, not relying on clinical history or other risk factors.
- 3In a large cohort (over 2 million images, thousands of patients), the AI achieved an R² of 0.95 for term and 0.92 for all births.
- 4Continuous retraining improved AI's prediction of preterm births, with R² increasing from 0.48 (V1) to 0.72 (V4).
- 5Technology is scalable, non-invasive, and functions well across all trimesters and diverse patient populations.
Why It Matters
Accurate prediction of delivery timing, especially preterm births, remains a persistent challenge. This AI-driven approach, requiring only routine ultrasound images, could significantly improve pregnancy management and outcomes worldwide, particularly in under-resourced settings.
Source
EurekAlert
Related News
•EurekAlert
Deep Learning Enables Virtual Multiplexed Immunostaining in Pathology
Researchers developed a deep learning framework for generating virtual immunostaining on label-free tissue to assess vascular invasion in thyroid cancer.
•EurekAlert
AI and Ground-Penetrating Radar Innovate Detection of Hidden Steel Damage
University of Houston researchers developed an AI and radar-based method to detect hidden damage in cold-formed steel used in building structures.
•EurekAlert
WPI Team Achieves 93% Accuracy in MRI-AI Alzheimer's Prediction
Worcester Polytechnic Institute developed a machine learning tool using MRI scans to predict Alzheimer’s disease with nearly 93% accuracy, uncovering significant age and sex differences in brain atrophy patterns.