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

作者：David Charles Klonoff, Richard M. Bergenstal, Eda Cengiz, Mark Allen Clements, Daniel Espes, Juan Espinoza, David Kerr, Boris P. Kovatchev, David M. Maahs, Julia Katharina Mader, Nestoras Nicolas Mathioudakis, Ahmed A. Metwally, Shahid N. Shah, Bin Sheng, Michael Snyder, Guillermo E. Umpierrez, Mandy M. Shao, Agatha F. Scheideman, Alessandra T. Ayers, Cindy Ho, Elizabeth A. Healey · 发表于：Journal of Diabetes Science and Technology · 年份：2025 · DOI：10.1177/19322968251353228 · 被引用次数：21 · 研究领域：Diabetes Management and Research、Pancreatic function and diabetes、Diet and metabolism studies

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.