Innovative digital twin framework for early risk detection and personalized perinatal healthcare

Pregnancy-related complications such as gestational diabetes mellitus (GDM) and preeclampsia require timely identification, as initially low-risk pregnancies may develop clinically relevant risks during follow-up. Digital Health and Telemedicine can support remote monitoring, while Digital Twin (DT) technology offers a framework for integrating longitudinal patient data with predictive models. However, DT-based perinatal systems remain exploratory and require methodological validation before clinical use.

This study presents a proof-of-concept DT framework for perinatal monitoring. Three data roles are distinguished: public benchmark datasets for model training and testing, synthetic patient records generated with Synthea to simulate prenatal consultations, and synthetic time-stamped wearable-like records generated with Gretel.ai to emulate repeated monitoring at the data-acquisition layer. Clinical and wearable-like streams were processed independently and combined only at the interpretation stage.

The maternal risk model performed better for low- and high-risk classes than for the intermediate class, supporting its use as a screening-oriented component rather than a definitive classifier. GDM models achieved high recall for the GDM class in both clinical and wearable-like configurations. By contrast, preeclampsia prediction showed limited performance, especially with wearable-accessible blood pressure variables alone; the clinical model improved the class profile but remained insufficient for clinical deployment. The simulated patient cases illustrate the DT workflow and the limitations of wearable-only inputs for complex obstetric risk stratification.

The proposed framework should be interpreted as a methodological prototype for organizing clinical and time-stamped wearable-like data within a perinatal DT. The results support combining episodic clinical information with repeated physiological monitoring, but also show that model calibration, class imbalance, richer clinical features, and validation with real longitudinal cohorts are needed before clinical decision-support use. Future work will focus on real-world validation, sequence-aware modelling, and integration with clinical workflows under medical supervision.