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Updated: Jul 1, 2026

Deep Neural Networks for Image-Based Dietary Assessment
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Improving Multi-Sensor Non-Invasive Glucose Detection through AI: A Domain Generalization Approach.

Yuyang Sun, Helena Cano-Garcia, Eleonora Razzicchia

    IEEE Journal of Biomedical and Health Informatics
    |June 29, 2026
    PubMed
    Summary
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    This study introduces meta-forests for accurate non-invasive glucose monitoring in diabetes management. The novel approach addresses patient variability, achieving results comparable to current methods.

    Area of Science:

    • Biomedical Engineering
    • Data Science
    • Medical Diagnostics

    Background:

    • Accurate glucose monitoring is vital for diabetes management and preventing complications.
    • Inter-patient variability presents a significant challenge for non-invasive glucose monitoring systems.
    • Existing methods struggle to generalize across diverse patient populations.

    Purpose of the Study:

    • To develop and evaluate a novel domain generalization approach, meta-forests, for accurate non-invasive glucose monitoring.
    • To address the challenge of inter-patient heterogeneity in glucose level prediction.
    • To enhance the interpretability of non-invasive glucose detection models.

    Main Methods:

    • Utilized a dataset of 54,280 data points from five subjects over 10 days.

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  • Employed a non-invasive system integrating near-infrared (NIR) spectroscopy, millimeter-wave (mm-wave) sensing, and temperature measurements.
  • Applied meta-forests, an ensemble-based domain generalization technique, and incorporated Shapley additive explanations (SHAP) for model interpretability.

    • Main Results:

    • Achieved an average root mean square error (RMSE) of 1.10 mmol/L.
    • Attained a mean absolute percentage error (MAPE) of 10.32% in subject-specific experiments.
    • Demonstrated accuracy comparable to state-of-the-art non-invasive glucose detection methods.

    Conclusions:

    • Meta-forests effectively address inter-patient heterogeneity in non-invasive glucose monitoring.
    • The developed system offers a promising approach for accurate and reliable glucose level detection.
    • Enhanced model interpretability through SHAP analysis provides valuable insights into the prediction process.