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

Donghyeok Lee, Annemiek Maaskant*, Huy Ngo, Roy C. Montijn, Jaco Bakker, Jan A.M. Langermans, Evgeni Levin*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

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 (Macaca mulatta). 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.

Original languageEnglish
Article number111310
Number of pages10
JournaliScience
Volume27
Issue number12
DOIs
Publication statusPublished - 20 Dec 2024

Bibliographical note

Publisher Copyright:
© 2024 The Author(s)

Keywords

  • Biological sciences
  • Microbiology
  • Microbiome

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