Fetal Biometry

Fetal biometry uses ultrasound imaging to measure fetal dimensions (head, abdomen, femur, etc.) for assessing gestational age, growth, and detecting anomalies. Current research heavily employs deep learning, particularly convolutional neural networks (CNNs) and U-Net architectures, often incorporating techniques like multi-task learning, Bayesian methods, and uncertainty modeling to improve accuracy and reliability of automated biometric measurements from both still images and video scans. These advancements aim to reduce inter-observer variability, improve diagnostic accuracy, and potentially streamline clinical workflows, particularly in resource-limited settings where expert sonographers are scarce. The ultimate goal is to provide more precise and efficient fetal assessments, leading to better prenatal care and improved pregnancy outcomes.