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<p>Diabetes mellitus is a chronic disorder characterized by persistent hyperglycemia that damages multiple organs. With global prevalence rising, accurate, convenient, and non-invasive glucose monitoring is urgently needed. However, current diagnostic methods—such as point-sample tests and continuous glucose monitoring (CGM)—remain limited by invasiveness and potential inaccuracy. In this study, we conduct an in vitro feasibility evaluation toward non-invasive glucose monitoring using an 80 MHz high-frequency ultrasound (HFU) transducer paired with a convolutional neural network (CNN). Signals were converted into time-frequency representations and analyzed using CNNs to classify blood samples by glucose concentration. Despite the inherent heterogeneity and noise in whole blood, the system achieved approximately 68% multi-class accuracy across glucose levels. These results indicate that ultrasound-based, AI-driven signal analysis offers a promising alternative for continuous, non-invasive glucose assessment, supporting improved diabetes management in clinical and home settings.</p>

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AI-assisted ultrasonic system for non-invasive glucose classification in whole blood

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月25日

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  • Jeong Eun Lee,
  • Hyeon-Ju Jeon,
  • Min-Seo Kim,
  • Taeyang Kwon,
  • O-Joun Lee,
  • Hae Gyun Lim

摘要

Diabetes mellitus is a chronic disorder characterized by persistent hyperglycemia that damages multiple organs. With global prevalence rising, accurate, convenient, and non-invasive glucose monitoring is urgently needed. However, current diagnostic methods—such as point-sample tests and continuous glucose monitoring (CGM)—remain limited by invasiveness and potential inaccuracy. In this study, we conduct an in vitro feasibility evaluation toward non-invasive glucose monitoring using an 80 MHz high-frequency ultrasound (HFU) transducer paired with a convolutional neural network (CNN). Signals were converted into time-frequency representations and analyzed using CNNs to classify blood samples by glucose concentration. Despite the inherent heterogeneity and noise in whole blood, the system achieved approximately 68% multi-class accuracy across glucose levels. These results indicate that ultrasound-based, AI-driven signal analysis offers a promising alternative for continuous, non-invasive glucose assessment, supporting improved diabetes management in clinical and home settings.