A rapid, affordable, and reliable method for profiling microbiome biomarkers from fecal images.

Human and veterinary healthcare professionals are interested in utilizing the gut-microbiome as a target to diagnose, treat, and prevent (gastrointestinal) diseases. However, the current microbiome analysis techniques are expensive and time-consuming, and data interpretation requires the expertise of specialists. Therefore, we explored the development and application of artificial intelligence technology for rapid, affordable, and reliable microbiome profiling in rhesus macaques (). Tailor-made learning algorithms were created by integrating digital images of fecal samples with corresponding whole-genome sequenced microbial profiles. These algorithms were trained to identify alpha-diversity (Shannon index), key microbial markers, and fecal consistency from the digital images of fecal smears. A binary classification strategy was applied to distinguish between samples with high and low diversity and presence or absence of selected bacterial genera. Our results revealed a successful proof of concept for "high and low" prediction of diversity, fecal consistency, and "present or absent" for selected bacterial genera.