Lim Pei Ying, Oh Xin Yin, Ong Wei Quan, Neha Jain, Jayashree Mayuren, Manisha Pandey, Bapi Gorain, Mayuren Candasamy
Keywords
Continuous glucose monitoring (CGM) • Artifi cial intelligence (AI) • Machine learning (ML) • Glycemic event prediction
Background Diabetes mellitus is a chronic metabolic disorder that affects 537 million of the population worldwide whereby continuous glucose monitoring (CGM) has been implemented in the management of diabetes.
Introduction CGM tracks glucose levels for 24 h without interruption via sensor detection which provides a large data set for blood glucose prediction in diabetic patients. By incorporating the Internet-of-Things healthcare systems into wearable CGM devices, the artificial intelligence-based CGM models facilitate diabetes management by assisting with blood glucose trend analysis, blood glucose profile and diabetic risk prediction, early warning of the potential glycemic events predicted, and insulin dose optimization.
Conclusion The development of AI-based technology has improved the overall outcome of diabetes management. The AI algorithms with different approaches are helpful in clinical decision-making and health-related data tracking, particularly in diabetes glucose management.
