Perinatal Artificial Intelligence Applications for Predicting High-Risk Pregnancy Complications Early

Abstract

Background:
High-risk pregnancy complications, including preeclampsia, gestational diabetes, preterm birth, and fetal growth restriction, remain major contributors to maternal and neonatal morbidity and mortality worldwide. Recent advances in artificial intelligence (AI) have enabled the development of predictive models capable of analyzing large clinical datasets and identifying women at risk of complications during early pregnancy.

Objective:
To evaluate the effectiveness of artificial intelligence applications in the early prediction of high-risk pregnancy complications and their potential role in improving maternal and neonatal outcomes.

Methodology:
A retrospective observational study was conducted involving 200 pregnant women. Clinical, demographic, laboratory, and obstetric data were collected from electronic health records. Machine learning algorithms, including random forest, support vector machine, and neural network models, were applied to predict pregnancy complications. Model performance was assessed using accuracy, sensitivity, specificity, and area under the curve (AUC) metrics.

Findings:
The AI-based prediction model achieved an overall accuracy of 91.3%, sensitivity of 89.7%, specificity of 92.1%, and an AUC of 0.94. Early prediction rates were highest for preeclampsia (88.5%) and gestational diabetes (85.2%). The use of AI-assisted screening reduced delayed diagnosis by 41.6% and improved risk stratification among high-risk pregnancies.

Conclusion:
Artificial intelligence applications demonstrate strong potential for the early prediction of high-risk pregnancy complications. AI-driven decision-support systems can enhance prenatal risk assessment, facilitate timely interventions, and contribute to improved maternal and neonatal health outcomes.