Home | Publications | KZN+26a

Predicting Postprandial Glycemic Response From Meal Images, Clinical Variables, and Gut Microbiome Information

MCML Authors

Abstract

Predicting postprandial glycemic response (PPGR) is fundamental to personalized nutrition and type 2 diabetes management, yet existing approaches typically rely on manually reported dietary intake, limiting their scalability in free-living settings. We propose a multimodal framework that replaces manual dietary logging with image-derived macronutrient estimates and integrates them with clinical variables and gut microbiome information for personalized PPGR prediction. The framework jointly performs image-based macronutrient estimation and glucose prediction, while an attention-based prediction module models interactions between dietary and host-specific information. We evaluate the proposed approach on a real-world dataset comprising meal images, continuous glucose monitoring, clinical variables, and gut microbiome profiles. The proposed model outperforms existing PPGR baselines using image-derived nutritional inputs and approaches the performance of methods that rely on manually reported macronutrients despite using automatically estimated nutritional information. These results demonstrate that combining image-derived nutrition with complementary clinical and gut microbiome information provides a practical foundation for scalable personalized PPGR prediction.

inproceedings KZN+26a

MultiTab @MICCAI 2026

Workshop on Multimodal Learning with Medical Tabular Data at the 29th International Conference on Medical Image Computing and Computer Assisted Intervention. Strasbourg, France, Sep 27-Oct 01, 2026. To be published. Preprint available.

Authors

V. Kondratyeva • K. Zaripova • N. Navab • A. Farshad

Links

URL

Research Area

 C1 | Medicine

BibTeXKey: KZN+26a

#p-navab
Back to Top