Continuous Glucose Monitoring Data Analysis 2.0: Functional Data Pattern Recognition and Artificial Intelligence Applications.
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Klonoff DC, Bergenstal RM, Cengiz E, Clements MA, Espes D, Espinoza J, Kerr D, Kovatchev B, Maahs DM, Mader JK, Mathioudakis N, Metwally AA, Shah SN, Sheng B, Snyder MP, Umpierrez G, Shao MM, Scheideman AF, Ayers AT, Ho CN, Healey E
J Diabetes Sci Technol 19 (6) 1515-1527 [2025-11-00; online 2025-08-14]
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.
PubMed 40814224
DOI 10.1177/19322968251353228
Crossref 10.1177/19322968251353228