Continuous Glucose Monitoring Data Analysis 2.0: Functional Data Pattern Recognition and Artificial Intelligence Applications.

Document Type

Article

Publication Date

11-2025

Identifier

DOI: 10.1177/19322968251353228; PMCID: PMC12356821

Abstract

New methods of continuous glucose monitoring (CGM) data analysis are emerging that are valuable for interpreting CGM patterns and underlying metabolic physiology. These new methods use functional data analysis and artificial intelligence (AI), including machine learning (ML). Compared to traditional metrics for evaluating CGM tracing results (CGM Data Analysis 1.0), these new methods, which we refer to as CGM Data Analysis 2.0, can provide a more detailed understanding of glucose fluctuations and trends and enable more personalized and effective diabetes management strategies once translated into practical clinical solutions.

Journal Title

J Diabetes Sci Technol

Volume

19

Issue

6

First Page

1515

Last Page

1527

MeSH Keywords

Humans; Blood Glucose Self-Monitoring; Blood Glucose; Artificial Intelligence; Machine Learning; Pattern Recognition, Automated; Diabetes Mellitus; Continuous Glucose Monitoring

PubMed ID

40814224

Keywords

CGM; artificial intelligence; diabetes; machine learning; pattern analysis

Comments

Grants and funding

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