Development of an Artificial Intelligence Assisted Microbiome Response Model for Optimizing Fecal Microbiota Transplantation Probiotic and Postbiotic Interventions in Ulcerative Colitis and Crohn’s Disease

Abstract

Inflammatory bowel diseases (IBD), including ulcerative colitis and Crohn’s disease, are chronic immune-mediated disorders characterized by recurrent intestinal inflammation, disruption of gut microbial homeostasis, and heterogeneous therapeutic responses. Although fecal microbiota transplantation (FMT), probiotic supplementation, and postbiotic interventions have emerged as promising microbiome-based therapeutic strategies, substantial variability exists in patient outcomes. This variability reflects the complex interactions among microbial communities, host immune pathways, metabolic signaling networks, disease phenotype, and environmental influences. Consequently, the identification of individualized therapeutic approaches remains a major challenge in contemporary gastroenterology. The present study proposes an artificial intelligence assisted microbiome response model designed to optimize microbiome-targeted interventions in patients with ulcerative colitis and Crohn’s disease. The proposed framework integrates multidimensional clinical, microbiological, immunological, and metabolic datasets to predict treatment responsiveness and support precision therapeutic decision-making. A comprehensive dataset derived from published clinical investigations involving FMT, probiotic administration, and postbiotic therapies was utilized to construct predictive algorithms capable of identifying microbial signatures associated with remission, mucosal healing, and sustained clinical improvement. Machine learning techniques were applied to evaluate relationships between microbial diversity indices, dominant bacterial taxa, inflammatory biomarkers, treatment modalities, and patient outcomes. The model was structured to classify patients according to predicted responsiveness and to generate individualized intervention recommendations based on microbiome characteristics. Comparative analyses demonstrated that integrated microbiome-based prediction significantly improved the identification of favorable therapeutic pathways compared with conventional symptom-oriented approaches. Furthermore, microbial network reconstruction and metabolite-associated patterns emerged as important determinants of long-term treatment success. The findings highlight the potential of artificial intelligence to transform microbiome-guided management of inflammatory bowel disease by enabling personalized selection of FMT donors, probiotic formulations, and postbiotic strategies. The proposed framework contributes to the advancement of precision medicine by providing a scalable and clinically adaptable approach for optimizing therapeutic efficacy while reducing uncertainty in treatment selection. Future implementation of validated artificial intelligence models may facilitate more effective microbiome modulation strategies and improve long-term outcomes in patients with ulcerative colitis and Crohn’s disease.