What question did this study set out to answer?

This research aims to create a framework for digital twins of biosensors that can provide real-time hormone monitoring.

March 16, 2026Open Access

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

Key Points

This research aims to create a framework for digital twins of biosensors that can provide real-time hormone monitoring.
Developed a framework for data-driven digital twins of biosensors.
Integrated digital twins into smart health monitoring systems.
Focused on personalized infertility care using the digital twin technology.
Showcased the practicality of digital twin frameworks in hormone biosensing.
Indicated potential for real-time, adaptive health monitoring.
Laid the groundwork for precision medicine applications in infertility care.

Abstract

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.

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

Key Points

Abstract

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