Application of image guided analyses to monitor fecal microbial composition and diversity in a human cohort.

The critical role of gut microbiota in human health and disease has been increasingly illustrated over the past decades, with a significant amount of research demonstrating an unmet need for self-monitor of the fecal microbial composition in an easily-accessible, rapid-time manner. In this study, we employed a tool for Smartphone Microbiome Evaluation and Analysis in Rapid-time (SMEAR) that uses images of fecal smears to predict microbial compositional characteristics in a human cohort. A subset of human fecal samples was randomly retrieved from the second wave of data collection in the Healthy Life in an Urban Setting (HELIUS) study cohort. Per sample, 16S rRNA gene sequencing data was generated in addition to an image of a fecal smear, spread on a standard A4 paper. Metagenomics-paired pictures were used to validate a computer vision-based technology to classify whether the sample is of low or high relative abundance of the 50 most abundant genera, and α-diversity (Shannon-index). In total, 888 fecal samples were used as an application of the SMEAR technology. SMEAR gave accurate predictions whether a fecal sample is of low or high relative abundance of Sporobacter, Oscillibacter and Intestinimonas (very good performance, AUC > 0.8, p-value < 0.001, for all models), as well as Neglecta, Megasphaera, Lachnospira, Methanobrevibacter, Harryflintia, Roseburia, and Dialister (good performance, AUC > 0.75, p-value < 0.001, for all models). Likewise, SMEAR could classify whether a fecal sample was of low or high α-diversity (AUC = 0.83, p-value < 0.001). Our study demonstrates that SMEAR robustly predicts microbial composition and diversity from digital images of fecal smears in a human cohort. These findings establish SMEAR as a new benchmark for rapid, cost-effective microbiome diagnostics and pave the way for its direct application in research settings and clinical validation.