Continuous Glucose

Continuous glucose monitoring (CGM) aims to provide accurate and timely blood glucose level measurements, primarily for managing diabetes. Current research focuses on developing non-invasive CGM methods using diverse data sources like voice signals, near-infrared spectroscopy, and electrocardiograms, often employing machine learning models such as neural networks (including transformers and recurrent networks), federated learning, and ensemble methods for improved accuracy and personalization. These advancements hold significant promise for improving diabetes management by enabling more precise insulin delivery, personalized treatment strategies, and earlier detection of hypo- and hyperglycemic events, ultimately enhancing patient outcomes and reducing healthcare costs.