Lee Donghyeok, Maaskant Annemiek, Ngo Huy, Montijn Roy C, Bakker Jaco, Langermans Jan A M, Levin Evgeni
HORAIZON Technology BV, Marshallaan 2, 2625 GZ Delft, the Netherlands.
Biomedical Primate Research Centre, Lange Kleiweg 161, 2288 7 GJ Rijswijk, the Netherlands.
iScience. 2024 Nov 8;27(12):111310. doi: 10.1016/j.isci.2024.111310. eCollection 2024 Dec 20.
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.
人类和兽医医疗保健专业人员有兴趣将肠道微生物群作为诊断、治疗和预防（胃肠道）疾病的靶点。然而，目前的微生物群分析技术昂贵且耗时，数据解读需要专家的专业知识。因此，我们探索了人工智能技术在恒河猴中进行快速、经济且可靠的微生物群分析的开发和应用。通过将粪便样本的数字图像与相应的全基因组测序微生物图谱相结合，创建了量身定制的学习算法。这些算法经过训练，可从粪便涂片的数字图像中识别α多样性（香农指数）、关键微生物标志物和粪便稠度。应用二元分类策略来区分高多样性和低多样性样本以及选定细菌属的存在与否。我们的结果揭示了对多样性、粪便稠度进行“高和低”预测以及对选定细菌属进行“存在或不存在”预测的概念验证的成功。
