Developing and validating an artificial intelligence-based application for predicting some pregnancy outcomes: a multi-phase study protocol.

BACKGROUND

Pregnancy complications such as preterm birth, low birth weight, gestational diabetes mellitus, preeclampsia, and intrauterine growth restriction significantly affect both maternal and neonatal health outcomes. Early identification of high-risk pregnancies is essential for timely interventions; however, traditional predictive models often lack accuracy. This study aims to develop and validate an AI-based application to improve risk assessment and clinical decision-making regarding pregnancy outcomes through a multi-phase approach.

METHODS

This study comprises three phases. In Phase 1, retrospective case-control data will be collected from medical records, including Mother and Infant System (IMaN), Hospital Information System (HIS), and archived records of women who gave birth at Al-Zahra and Taleghani Educational and Medical Centers in Tabriz between 2022 and 2024. In Phase 2, an artificial intelligence model will be developed using machine learning algorithms such as Random Forest, XGBoost, Support Vector Machines (SVM), and neural networks, followed by model training, validation, and integration into a user-friendly application. Phase 3 will focus on a prospective cohort study of pregnant women attending clinics after 22 weeks of gestation, evaluating the AI model’s predictive performance through metrics like AUROC (area under the receiver operating characteristic curve), sensitivity, specificity, and predictive values, along with real-time data collection. Content validity will be determined through expert reviews. DISCUSSION: This study protocol presents a multi-phase approach to developing and validating an AI-based application for predicting pregnancy outcomes. By integrating retrospective data analysis, machine learning, and prospective validation, the study aims to improve early risk detection and maternal care. If successful, this application could support personalized obstetric decision-making.