Predicting Postprandial Glycemic Response from Meal Images, Clinical Variables, and Gut Microbiome Information
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 propos...
| Authors | Varvara Kondratyeva, Kamilia Zaripova, Nassir Navab et al. |
|---|---|
| Primary category | cs.AI |
| Published | 21 September 2026 |
| Last updated at source | 21 September 2026 |
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