Toward a Machine Learning-Driven Digital Twin for Real-Time Hormone Biosensing in Personalized Infertility Care

Abstract

Background: The increasing demand for personalized healthcare solutions highlights the limitations of one-size-fits-all treatment strategies. Digital twin (DT) technology, which enables real-time virtual replicas of physical systems, offers a promising approach to advance personalized medicine through continuous monitoring, simulation, and prediction. Methods: This study presents the foundational phase of a machine-learning-driven biosensor DT designed to support personalized infertility treatment through integration with a Smart Health Monitoring System. The DT replicates the behavior of a field-effect transistor (FET)-based biosensor functionalized with 17β-estradiol aptamers and trained on experimental data obtained from silicon nanonet BioFET prototypes. Results: Seven supervised machine learning algorithms were evaluated to predict hormone concentration from electrical parameters (Vg, Isd). The K-Nearest Neighbors (KNN) model achieved the highest predictive accuracy (R2 = 0.99, CV-R2 = 0.98, RMSE = 11.87 pg/mL) and demonstrated robust cross-device generalization under Leave-One-Biosensor-Out validation (R2 = 0.59). These results confirm the model's capability to capture nonlinear relationships and generalize across independently fabricated sensors. Conclusion: The developed model constitutes a validated predictive core of a biosensor digital twin. At the current stage, the DT is limited to predictive modeling and does not yet implement real-time synchronization or closed-loop feedback, which are planned in future work. Significance: This study establishes a practical framework for data-driven digital twins of biosensors and demonstrates their potential for integration into smart health monitoring systems supporting personalized infertility care. The proposed approach provides a foundation for real-time, adaptive, and clinically relevant biosensor twins in precision medicine.