Roles and applications of artificial intelligence in fetal and placental MRI: a literature review

BACKGROUND: Artificial intelligence (AI) has been increasingly integrated with fetal and placental magnetic resonance imaging (MRI) to enhance the detection of abnormalities and streamline diagnostic processes. MRI, known for its superior soft tissue contrast and multiplanar imaging capabilities, is a critical tool for evaluating complex fetal and placental conditions. However, the large volume of MRI data often poses challenges for clinicians in providing timely and accurate diagnoses. Fetal and placental MRI are subject to several inherent limitations, including fetal motion artifacts, low signal-to-noise ratio, and operator-dependent variability, which can reduce image quality and hinder accurate diagnosis. Artificial intelligence has shown significant potential in addressing these challenges. For example, AI-based methods have been applied to motion artifact reduction, automated organ segmentation, and disease classification. In addition, recent studies have demonstrated that AI can reduce MRI scan times by up to 60% without compromising image quality, thereby improving diagnostic accuracy and workflow efficiency. METHODS: A systematic PubMed search was conducted on January 16, and May 20, 2024, using predefined terms related to fetal and placental MRI and AI. Peer-reviewed English studies were included, while irrelevant articles, reviews, and editorials were excluded. Disagreements during the review process were resolved by a third reviewer. RESULTS: After systematic screening, 74 studies on AI applications in fetal MRI and 32 studies on placental MRI were included. Key applications in fetal MRI included motion correction (17.6%), organ segmentation (48.6%), and disease classification (6.8%). For placental MRI, studies primarily focused on placental invasion assessment (53.1%) and segmentation (28.1%). Relevant studies published between 2016 and 2024 were categorized by application area and analyzed in detail. CONCLUSIONS: This review synthesizes extensive research on AI applications in fetal and placental MRI, highlighting its potential to enhance imaging quality, automate tasks such as segmentation and motion correction, and improve diagnostic accuracy. However, challenges remain, including reliance on small, single-center datasets, limited demographic and pathological diversity, and a predominance of Two-Dimensional (2D) imaging techniques. Addressing these issues through the development of diverse, multi-center datasets and the exploration of advanced Three-Dimensional (3D) imaging methods is essential. By overcoming these barriers and integrating multimodal approaches, AI holds immense promise for revolutionizing prenatal diagnostics and advancing personalized care.