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Related Experiment Video

Updated: Jul 16, 2025

Author Spotlight: Modeling an Aspect of Preeclampsia in Female Mice Using Hypoxic Human Placenta-Derived Small Extracellular Vesicles

Author Spotlight: Modeling an Aspect of Preeclampsia in Female Mice Using Hypoxic Human Placenta-Derived Small Extracellular Vesicles

Published on: January 26, 2024

Machine learning models for predicting pre-eclampsia: a systematic review protocol.

Amene Ranjbar¹, Elham Taeidi², Vahid Mehrnoush²

¹Fertility and Infertility Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran.

|September 11, 2023

Summary

This summary is machine-generated.

This systematic review identifies machine learning predictive factors for pre-eclampsia. It evaluates the diagnostic accuracy of these AI models in predicting pre-eclampsia, a major pregnancy complication.

Keywords:

maternal medicine obstetrics prenatal diagnosis

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Area of Science:

Obstetrics and Gynecology
Medical Informatics
Computational Biology

Background:

Pre-eclampsia is a significant global cause of maternal mortality during pregnancy.
Identifying predictive factors and accurate diagnostic tools for pre-eclampsia is crucial for improving maternal outcomes.

Purpose of the Study:

To systematically review and summarize predictive factors of pre-eclampsia identified through machine learning models.
To evaluate the diagnostic accuracy of machine learning models in predicting pre-eclampsia.

Main Methods:

Adherence to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
Comprehensive literature search from inception to January 2023 across major databases (PubMed, EMBASE, Scopus, etc.).
Inclusion of studies using machine learning for pre-eclampsia prediction; exclusion of non-English and unrelated articles. Risk of bias assessed using PROBAST.

Main Results:

The review synthesizes findings on various machine learning algorithms and features used for pre-eclampsia prediction.
Diagnostic accuracy metrics (sensitivity, specificity, AUC) of identified models are evaluated.
Key predictive factors identified by machine learning models are summarized.

Conclusions:

Machine learning models show promise in identifying predictive factors and improving the diagnostic accuracy of pre-eclampsia.
Further research and validation of these models are essential for clinical implementation to reduce maternal mortality.