Obstetric ultrasound, a non-invasive and cost-effective imaging technique, plays a pivotal role in assessing fetal biometry for evaluating growth and well-being during pregnancy. Accurate estimation of fetal weight is crucial for determining appropriate obstetric management. The standard procedure involves measuring biparietal diameter, head circumference, abdominal circumference, and femur length, but it is subject to variability and dependence on operator expertise.

Addressing these challenges, the application of artificial intelligence (AI) in obstetric ultrasound has emerged. AI, particularly machine learning algorithms, is increasingly employed to automate fetal biometry on standardized planes, potentially minimizing variability and enhancing efficiency. These algorithms analyze ultrasound images, extracting relevant features to estimate fetal weight. The use of deep learning architectures, such as convolutional neural networks (CNNs), has shown promising results. By leveraging machine learning and deep learning techniques, these systems aim to provide more reliable predictions of fetal weight, contributing to enhanced monitoring and management of pregnancy. The potential benefits include increased efficiency, reduced observer-dependency, and improved precision in assessing fetal growth and well-being. However, integration into clinical practice requires rigorous testing, validation, regulatory approval, and acceptance by healthcare professionals.