CGM Data Analysis 2.0: Functional Data Pattern Recognition and Artificial Intelligence Applications
Quantitative Methods
2025-05-14 v1
Abstract
New methods of 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.
Cite
@article{arxiv.2505.07885,
title = {CGM Data Analysis 2.0: Functional Data Pattern Recognition and Artificial Intelligence Applications},
author = {David C. Klonoff and Richard M. Bergenstal and Eda Cengiz and Mark A. Clements and Daniel Espes and Juan Espinoza and David Kerr and Boris Kovatchev and David M. Maahs and Julia K. Mader and Nestoras Mathioudakis and Ahmed A. Metwally and Shahid N. Shah and Bin Sheng and Michael P. Snyder and Guillermo Umpierrez and Alessandra T. Ayers and Cindy N. Ho and Elizabeth Healey},
journal= {arXiv preprint arXiv:2505.07885},
year = {2025}
}