Abstract
Background: Pre-eclampsia (PE) remains a leading cause of maternal and perinatal morbidity and mortality worldwide. Early detection and risk stratification are critical for improving pregnancy outcomes. This review aims to summarize current advancements in PE screening, including clinical risk factors, biomarkers, imaging techniques, and predictive models. Methods: A comprehensive literature search was conducted using PubMed, Scopus, Web of Science, and Google Scholar to identify relevant studies on PE screening and prediction. Peer-reviewed original studies, systematic reviews, and meta-analyses published in English were included, while case reports and conference abstracts were excluded. Results: Traditional screening methods rely on maternal history and clinical risk factors, while emerging approaches incorporate biochemical markers and ultrasound parameters to enhance predictive accuracy. Machine learning models and artificial intelligence (AI)-driven algorithms are being explored for improved risk stratification. However, challenges such as data heterogeneity, lack of external validation, and integration into clinical practice remain. Conclusions: Advances in PE screening hold promise for early identification and targeted prevention strategies. Future research should focus on validating predictive models in diverse populations, integrating AI with traditional screening methods, and developing personalized approaches to reduce PE-associated complications.