AI for rapid identification of major butyrate-producing bacteria in rhesus macaques (Macaca mulatta)

作者：Annemiek Maaskant, Donghyeok Lee, Huy Ngo, R.C. Montijn, Jaco Bakker, Jan A. M. Langermans, Evgeni Levin · 发表于：Animal Microbiome · 年份：2025 · DOI：10.1186/s42523-025-00410-2 · 被引用次数：2 · 研究领域：Gut microbiota and health、Fecal contamination and water quality、Colorectal Cancer Screening and Detection

BACKGROUND: The gut microbiome plays a crucial role in health and disease, influencing digestion, metabolism, and immune function. Traditional microbiome analysis methods are often expensive, time-consuming, and require specialized expertise, limiting their practical application in clinical settings. Evolving artificial intelligence (AI) technologies present opportunities for developing alternative methods. However, the lack of transparency in these technologies limits the ability of clinicians to incorporate AI-driven diagnostic tools into their healthcare systems. The aim of this study was to investigate an AI approach that rapidly predicts different bacterial genera and bacterial groups, specifically butyrate producers, from digital images of fecal smears of rhesus macaques (Macaca mulatta). In addition, to improve transparency, we employed explainability analysis to uncover the image features influencing the model's predictions. RESULTS: By integrating fecal image data with corresponding metagenomic sequencing information, the deep learning (DL) and machine learning (ML) algorithms successfully predicted 16 individual bacterial genera (area under the curve (AUC) > 0.7) among the 50 most abundant genera in rhesus macaques (Macaca mulatta). The model was successful in predicting functional groups, major butyrate producers (AUC 0.75) and a mixed group including fermenters and short-chain fatty acid (SCFA) producers (AUC 0.81). For both models of butyrate producers and mixed ...