TY - JOUR
T1 - A rapid, affordable, and reliable method for profiling microbiome biomarkers from fecal images
AU - Lee, Donghyeok
AU - Maaskant, Annemiek
AU - Ngo, Huy
AU - Montijn, Roy C.
AU - Bakker, Jaco
AU - Langermans, Jan A.M.
AU - Levin, Evgeni
N1 - Publisher Copyright:
© 2024 The Author(s)
PY - 2024/12/20
Y1 - 2024/12/20
N2 - 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.
AB - 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.
KW - Biological sciences
KW - Microbiology
KW - Microbiome
UR - http://www.scopus.com/inward/record.url?scp=85209362212&partnerID=8YFLogxK
U2 - 10.1016/j.isci.2024.111310
DO - 10.1016/j.isci.2024.111310
M3 - Article
AN - SCOPUS:85209362212
SN - 2589-0042
VL - 27
JO - iScience
JF - iScience
IS - 12
M1 - 111310
ER -