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April 3, 2026Advanced Intelligent SystemsOpen Access

Machine Learning‐Driven Digital Twin of a Field‐Effect Transistor‐Based Hormone Biosensor for Real‐Time Estradiol Monitoring

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Authors

AGAnastasiia GorelovaUniversity of AlicanteAPAlexandra ParichenkoMax Bergmann Zentrum für BiomaterialienSHShirong HuangMax Bergmann Zentrum für Biomaterialien

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Overview

Demonstrates a machine learning approach to real-time estradiol monitoring in infertility, suggesting potential for personalized health care.

Key Points

  • The research aims to develop a machine learning-driven digital twin framework for real-time monitoring of estradiol levels using hormone biosensors.
  • Developed a digital twin framework for a field-effect transistor-based hormone biosensor.
  • Used machine learning techniques to analyze real-time estradiol data.
  • Applied a row-level learning strategy treating each timepoint as a training sample.
  • Evaluated model performance using leave-one-biosensor-out validation with CatBoost.
  • CatBoost model achieved the best performance with RMSE of 0.240 in log scale and 85.28 pg/mL in original concentration.
  • Prediction relied on temporal and signal-morphology features, indicating meaningful learning dynamics.
  • Synthetic signal generation was successfully demonstrated using real biosensor data for forward modeling.

Cite This Study

Gorelova et al. (2026) studied this question.

synapsesocial.com/papers/69cf5dc55a333a821460bb51https://doi.org/10.1002/aisy.202500950
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