Machine learning approach and internet of things technologies to unravel the complex interaction between...

Inflammatory bowel diseases (IBD) are chronic, relapsing inflammatory disorders with ulcerative colitis (UC) and Crohns disease (CD) representing the two major phenotypes. While these conditions share common features, they exhibit distinct clinical presentations, disease behaviors, and pathogenetic mechanisms, highlighting the complexity of IBD. The global incidence and prevalence of IBD have risen dramatically in recent decades, probably linked to environmental changes such as dietary habits, urbanization, and reduced microbial exposure during early life, highlighting the interplay between environmental and genetic factors in disease pathogenesis. However, genetic factors alone cannot fully explain disease onset, emphasizing the critical role of environmental and microbial influences. Dysbiosis, characterized by reduced microbial diversity, loss of beneficial commensals, and an overabundance of pathogenic taxa, has emerged as a hallmark of IBD. Recent research has increasingly focused on the functional consequences of dysbiosis, its impact on microbial metabolites and pathways that contribute to chronic inflammation and disease progression. Understanding the functional implications of multi-omics changes, rather than simply cataloguing compositional changes, is now a priority in IBD research. Using artificial intelligence to combine data from noninvasive multi-omics technologies offers a significant opportunity to explore interactions among individual omics. It could represent a shift in IBD research by showing the complex mechanisms behind disease. This approach may revolutionize diagnostics and treatments, improving the quality of life for patients through precision medicine. This review aims to provide a comprehensive assessment of current progress. It highlights critical challenges and suggests possible future directions.