What question did this study set out to answer?

To develop a predictive framework using AI for noninvasive risk stratification of preterm premature rupture of membranes (PPROM).

June 12, 2026npj Digital Medicine0 citationsOpen Access

Noninvasive PPROM risk stratification with explainable AI using routine antenatal CRP and albumin

STShidong TanObstetrics and Gynecology Hospital of Fudan University SWSihan WuChildren's Medical Center SWShengnan WuShanghai First Maternity and Infant Hospital

Key Points

To develop a predictive framework using AI for noninvasive risk stratification of preterm premature rupture of membranes (PPROM).
Utilized a dataset of 114,601 antenatal laboratory and ultrasound measurements.
Employed a multimodal AI model integrating maternal demographics, lab results, and ultrasound data.
Optimized XGBoost algorithm for prediction with AUC of 0.952.
Identified elevated CRP levels and decreased albumin as significant risk factors in late pregnancy.
SHAP analysis revealed dynamic risk patterns across gestation, ensuring model interpretability.
The AI-driven framework enhances accurate PPROM risk assessment.

Abstract

Abstract Premature rupture of membranes (PROM) and preterm PROM (PPROM) are significant obstetric complications, affecting 10–20% and 3% of pregnancies globally and constituting a major cause of preterm birth and neonatal morbidity. While intrauterine infection is a key underlying driver, timely identification of high-risk cases remains a clinical challenge. We developed a noninvasive artificial intelligence (AI) predictive framework using a large-scale dataset of 114,601 antenatal laboratory and ultrasound measurements. Our multimodal model integrated longitudinal clinical measures, including maternal demographics, laboratory results, and ultrasound data. The optimized XGBoost algorithm achieved high performance (AUC: 0.952) and identified elevated C-reactive protein (CRP) and decreased albumin levels in late pregnancy as top features, underscoring the critical roles of inflammation and oxidative stress. SHAP analysis ensured interpretability, unveiling dynamic risk patterns across gestation. This AI-driven approach provides a precise, noninvasive tool for late-gestation PPROM risk stratification, paving the way for translation into precision obstetric care.

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