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

Anastasiia Gorelova; Alexandra Parichenko; Shirong Huang; Santiago Meliá; Gianaurelio Cuniberti

doi:10.1109/tbme.2026.3674340

January 1, 2026IEEE Transactions on Biomedical EngineeringOpen Access

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

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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.

Implication

Analysis demonstrates potential of biosensor digital twins for personalized infertility care in real-time health monitoring.

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.

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Cite This Study

Gorelova et al. (2026) studied this question.

synapsesocial.com/papers/69b79da78166e15b153aaeb5 https://doi.org/10.1109/tbme.2026.3674340

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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