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Abstract

Preeclampsia is a serious pregnancy complication that poses significant risks to both maternal and fetal health, often leading to severe outcomes if not detected early. Traditional diagnostic methods rely on clinical observations and biomarkers, which may not always provide timely or accurate predictions. In recent years, deep learning models have emerged as powerful tools for analyzing complex, high-dimensional data, offering the potential to enhance preeclampsia prediction by identifying patterns and features not easily discernible by conventional techniques. A review of the literature reveals that different types of ML and DL models for preeclampsia prediction were published between 2018 and 2022 using a variety of input data such as maternal demographics, clinical records, and ultrasound images. Studies demonstrate promising results, with models achieving improved sensitivity, specificity, and overall accuracy compared to traditional methods. However, challenges such as data quality, interpretation of models, and generalizability across populations remain key areas for further research. This literature review highlights the current advancements in deep learning-based preeclampsia prediction and discusses the potential for future improvements in clinical applications.