Artificial Intelligence for the Prediction of Preeclampsia: Current Evidence, Comparison with Conventional Screening Models, and Future Perspectives.

Authors

Abstract

Preeclampsia remains one of the leading causes of maternal and perinatal morbidity and mortality worldwide. Although current first-trimester screening strategies have improved risk assessment, their predictive performance remains limited by the biological complexity and heterogeneity of the disease. Artificial intelligence (AI) has emerged as a promising approach capable of integrating multidimensional clinical and biological data to improve early prediction. This review aims to summarize current evidence regarding AI-based prediction models for preeclampsia, compare their performance with conventional screening strategies, and discuss future directions for clinical implementation. A narrative review of published studies evaluating machine learning and deep learning models for first-trimester prediction of preeclampsia was performed. Studies incorporating maternal characteristics, hemodynamic variables, biochemical biomarkers, imaging, radiomics, and multi-omics data were reviewed. Diagnostic performance, predictor variables, and validation strategies were critically compared. Several studies have reported improved predictive performance of AI models compared with conventional statistical approaches, particularly when multimodal datasets were incorporated. High-performing models achieved area under the receiver operating characteristic curve (AUC) values ranging from 0.84 to 0.92. Across studies, maternal clinical characteristics, mean arterial pressure, uterine artery pulsatility index, placental growth factor, and pregnancy-associated plasma protein-A were the most consistently identified predictors. However, direct comparisons remain limited by methodological heterogeneity. Emerging approaches incorporating inflammatory biomarkers, cell-free nucleic acids, radiomics, and multi-omics technologies showed encouraging results but currently lack sufficient prospective multicenter validation for routine clinical implementation. AI has considerable potential to improve first-trimester prediction of preeclampsia, although prospective multicenter validation, standardized reporting, and implementation studies remain necessary before routine clinical adoption. Future research should prioritize prospective multicenter validation, standardized data collection, explainable AI, and seamless integration into clinical workflows to facilitate implementation in precision obstetric care.

Tags

Topics