Artificial intelligence in fetal brain imaging: Advancements, challenges, and multimodal approaches for biometric and structural analysis

Abstract

Artificial intelligence (AI) is transforming fetal brain imaging by addressing key challenges in diagnostic accuracy, efficiency, and data integration in prenatal care. This review explores AI's application in enhancing fetal brain imaging through ultrasound (US) and magnetic resonance imaging (MRI), with a particular focus on multimodal integration to leverage their complementary strengths. By critically analyzing state-of-the-art AI methodologies, including deep learning frameworks and attention-based architectures, this study highlights significant advancements alongside persistent challenges. Notable barriers include the scarcity of diverse and high-quality datasets, computational inefficiencies, and ethical concerns surrounding data privacy and security. Special attention is given to multimodal approaches that integrate US and MRI, combining the accessibility and real-time imaging of US with the superior soft tissue contrast of MRI to improve diagnostic precision. Furthermore, this review emphasizes the transformative potential of AI in fostering clinical adoption through innovations such as real-time diagnostic tools and human-AI collaboration frameworks. By providing a comprehensive roadmap for future research and implementation, this study underscores AI's potential to redefine fetal imaging practices, enhance diagnostic accuracy, and ultimately improve perinatal care outcomes.