Abstract
Early neurodevelopmental assessment in children plays an important role in identifying factors associated with neurodevelopmental disorders, including autism spectrum disorders (ASD). Brain ultrasound imaging is commonly used in pediatric practice, although the interpretation of images may differ between specialists. In this work, a prototype system for automated analysis of pediatric brain ultrasound images was developed using a hybrid approach that combines U-Net and CNN for segmentation and classification tasks. The obtained results indicate that such an approach is technically feasible and may serve as a basis for further studies focused on neurodevelopmental risk factors.