AI-Driven Microbiome-Based Early Diagnosis for Cancer and Autoimmune DiseasesA Systematic Framework Integrating Multi-Omics Microbiome Data with Machine Learning for Precision Early Detection

Abstract

Despite the fact that early diagnosis is the most important factor in survival and long-term quality of life in the treatment of both oncology and autoimmune disorders, traditional diagnostic processes still rely on invasive biopsies, late-stage imaging, and non-specific serological markers, which often make the diagnosis of disease at a late stage when there has been significant tissue damage. In the decade since its discovery, it has become clear that the human microbiome, especially the gut, oral and mucosal microbial communities, serves as a dynamic biosensor of host physiology: a reflection of immune dysregulation and neoplastic transformation in the host that precedes clinical signs. This paper introduces an integrated tool that combines microbiome profiling (16S rRNA sequencing, shotgun metagenomics and metabolomic profiling) with artificial intelligence and machine learning models to facilitate non-invasive, early and cost-effective diagnosis of colorectal cancer, gastric cancer, rheumatoid arthritis and inflammatory bowel disease. We combine results from several cohort datasets, benchmark five classes of AI models (Random Forest, Support Vector Machines, XGBoost, Deep Neural Networks, a hybrid CNN-LSTM model), and present comparison results in terms of accuracy, sensitivity, specificity and AUC-ROC. The microbial taxa Fusobacterium nucleatum, Akkermansia muciniphila and the Firmicutes to Bacteroidetes ratio contributed most to our hybrid deep learning model, which demonstrated the highest diagnostic performance with a 93.6% accuracy and an AUC of 0.96. Biological mechanisms are discussed, the importance of features is introduced using explainable AI, current limitations such as cohort heterogeneity and batch effects are discussed, and a roadmap to clinical use of microbiome-AI diagnostic panels is proposed.