review · Alexandria Engineering Journal
Continuous glucose monitoring is vital for managing diabetes, a chronic condition marked by high blood glucose levels that can cause severe organ damage over time. Significant progress driven by both academic and industrial efforts over the last decade has advanced invasive, minimally invasive, and non-invasive sensing techniques. These systems rely on integrated electronic components, wireless communication tools, and energy harvesting methods, yet glucose biosensors still face critical performance obstacles and technical constraints. Integrating time-series records from continuous glucose monitors with artificial intelligence methods allows for the creation of precise diabetes management protocols. Such algorithmic approaches achieve accurate results over constrained prediction horizons. Further technological development remains necessary to meet clinical needs and successfully translate improved glucose tracking platforms into practical healthcare applications.
Diabetes can cause progressive organ damage, making consistent blood glucose monitoring essential for both diabetic and non-diabetic people. Examining developments in sensor components, wireless communication, and energy harvesting helps clarify how wearables can become more reliable. Integrating these devices with predictive artificial intelligence allows clinicians and individuals to forecast glucose trends accurately, providing the timely information required to prevent severe medical complications.
The findings inform medical device manufacturers and digital healthcare providers developing wearable biosensors and automated diabetes management tools. Combining sensor hardware with predictive artificial intelligence models offers clinical practitioners and patients more accurate, actionable treatment protocols. As this is an overview of the technological landscape, energy harvesting capabilities, and machine learning integrations, the technologies discussed span from existing commercial monitoring hardware to earlier-stage analytical and algorithmic concepts requiring clinical translation.
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Diabetes is a chronic condition that is characterized by high blood glucose levels and can cause damage to multiple organs over time. Continuous monitoring of glucose levels is essential for both diabetic and non-diabetic individuals. There have been major developments in glucose monitoring technology over the past decade, which have been driven by research and industry efforts. Despite these significant advancements, the area of glucose biosensors still faces significant challenges. This paper presents a comprehensive summary of the latest glucose monitoring technologies, including invasive, minimally invasive, and non-invasive methods. Subsequently, we bring together the electronic components, wireless communication technologies, and energy harvesting opportunities, along with the limitations and challenges associated with current glucose monitoring solutions. This is followed by highlighting the potential integration of health records generated by continuous glucose monitors and artificial intelligence (AI) techniques to define precise diabetes management protocols. This integration achieves accurate results with constrained prediction horizons employing a time series of continuous glucose readings. The paper emphasizes the need for further advancements in glucose monitoring technology to improve diabetes management and address the critical need in clinical practice for improved glucose monitoring technologies with translational implications.
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DOI: 10.1016/j.aej.2024.01.021
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