Digital Twin–Enabled Smart Health Monitoring for Reproductive Medicine: Integrating Hormone Biosensing and Physiological Data

Abstract

Female infertility remains a major clinical and societal challenge, while the results of assisted reproductive technologies remain limited, and one reason is episodic hormone monitoring. Currently available methods for monitoring fertility or the menstrual cycle rely on periodic blood tests or indirect physiological indicators obtained from wearable devices, which provide only partial or delayed insight of rapidly changing hormonal fluctuations.In this work, we present a Digital Twin–enabled Smart Hormone Monitoring System (SHMS) under development, designed to integrate continuous hormone biosensing with physiological data within a unified digital twin (DT) architecture to support personalized infertility treatment. The proposed SHMS combines: (i) a minimally invasive wearable biosensor for real-time measurement of 17β-estradiol in interstitial fluid, (ii) patient and clinician applications for remote visualization and monitoring, and (iii) a patient-specific digital twin, designed to combine models trained at the population level datasets with individual hormone measurements.As a first step toward this integration, we validate the hormone-sensing and sensor-level DT components under controlled laboratory conditions. The biosensor response was evaluated across physiologically relevant estradiol concentrations ranging from 0 to 1000 pg/mL. After signal preprocessing and feature extraction, multiple regression models were trained to estimate hormone concentration from electrical biosensor signals. Linear Regression achieved the lowest cross-validated error (CV-RMSE = 178.27 pg/mL), indicating superior generalization compared to ensemble-based approaches. When predictions were discretized into clinically relevant concentration classes, an overall classification accuracy of approximately 87% was obtained.Ongoing work focuses on integrating longitudinal physiological data from wearable devices into the patient DT, enabling multimodal modelling of menstrual-cycle dynamics and prospective personalization of fertility treatment. Together, these results establish the proposed SHMS as a scalable foundation for DT–driven reproductive health monitoring.

Keywords: Digital twin, Reproductive health, Biosensors, Machine learning, Infertility treatment, Clinical decision support

DOI: 10.54941/ahfe1007211