AUTOMATED FETAL DEVELOPMENT ASSESSMENT IN ULTRASOUND USING AI

Abstract

Accurate assessment of fetal development is essential for monitoring pregnancy, detecting congenital abnormalities, and ensuring maternal-fetal health. Ultrasound imaging is the primary modality for prenatal evaluation, but manual interpretation is time-consuming and subject to inter-operator variability. Artificial intelligence (AI) algorithms, particularly deep learning models, offer the potential for automated, objective, and precise fetal assessment. This paper reviews current AI-based methodologies for fetal development evaluation using ultrasound imaging, focusing on convolutional neural networks (CNNs), image segmentation techniques, and hybrid approaches. Challenges such as limited annotated datasets, variability in ultrasound quality, and model interpretability are discussed. The study highlights the potential of AI-driven systems to support clinicians, enhance diagnostic accuracy, and improve prenatal care.