Automated Analysis of Glucose Response Patterns in Type 1 Diabetes Using Machine Learning and Computer Vision

This study presents an automated and data-driven framework for analysing glucose response patterns in individuals with Type 1 diabetes by integrating machine learning and computer vision methodologies. The system leverages multimodal data inputs, including food images, continuous glucose monitoring (CGM) data, and time-series meal logs to model glycaemic variability and infer personalized dietary effects. Using a dataset comprising over eighty annotated meals from eight subjects, the framework extracts nutritional features from food images via convolutional neural networks (CNNs) with attention mechanisms and correlates them with postprandial glucose trajectories. The analysis reveals substantial inter-individual variability and identifies critical temporal and nutritional factors influencing glucose dynamics. Results demonstrate the system’s capability to detect patterns predictive of glycemic responses, enabling the development of tailored dietary recommendations. This approach offers a scalable tool for personalized diabetes management and paves the way for future integration into real-time decision support systems.

@article{Jaggi2025,
  title     = {Automated Analysis of Glucose Response Patterns in Type 1 Diabetes Using Machine Learning and Computer Vision},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Arjun Jaggi and Aditya Karnam Gururaj Rao and Sonam Naidu and Vijay Mane and Siddharth Bhorge and Medha Wyawahare},
  doi       = {10.14569/IJACSA.2025.0160502},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160502}
}