AMAL-For-Qatar: A Comprehensive AI Ecosystem for Fetal Ultrasound Analysis Project Overview and Achievements

The AMAL-For-Qatar project (Advancing Precision Medicine with AI-Mediated for Fetal Life through Ultrasound Video Analysis) represents a comprehensive initiative to develop an end-to-end artificial intelligence ecosystem for fetal ultrasound analysis. This paper provides a high-level overview of the project's achievements to date, encompassing validated datasets, advanced segmentation models, automated reporting systems, and systematic evaluations of emerging AI technologies. Our contributions include the largest publicly available annotated dataset for fetal head biometry (3,832 images), the FetSAM segmentation model achieving state-of-the-art performance (DSC 0.901), super-resolution techniques for low-resource settings, and the FADA automated reporting system. Additionally, we present comprehensive evaluations of vision-language models for ultrasound interpretation and a systematic review of publicly available fetal ultrasound databases. Ongoing research focuses on integrating these components into a comprehensive clinical decision support system while addressing critical safety and deployment challenges. These achievements establish a robust foundation for AI-assisted prenatal diagnostics and demonstrate the potential for improving maternal-fetal healthcare through precision medicine approaches.