This study developed a neural network model to estimate gestational age using blind ultrasound sweeps from pregnant women in North Carolina and Zambia. The model demonstrated greater accuracy in estimating gestational age than traditional fetal biometry, with a mean absolute error of 3.9 days compared to 4.7 days. These findings suggest the potential for AI-based ultrasound to improve access to quality obstetric care, particularly in low-resource settings.
Creator
University of North Carolina, University of Zambia School of Medicine, Warren Alpert Medical School of Brown University
Information
Pediatrics or Adult
Adult
Speciality
Radiology
Modality
Ultrasound
Training
4,695 blind ultrasound sweeps (cineloops) from pregnant volunteers in North Carolina and Zambia