CAAF: A Cross-Attention and Adaptive Fusion Framework for Automated Detection and Severity Grading of Fetal Ventriculomegaly in Ultrasound Imaging

Background: Fetal ventriculomegaly (VM), which is defined by abnormal ventricular size, is one of the most common brain malformations detected during fetal screening. Early and precise diagnostic tools are important in predicting neurodevelopment, but ultrasound examination is highly operator-dependent, thereby creating subjectivity in its application. Objective: To design a precise, reliable, and computationally efficient deep learning system for detecting fetal VM cases and grading their sever