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To tackle this challenge, it is important to design appropriate regimens based on data regarding the species identity of bacterial pathogen concerned, as well as their antimicrobial\u2010resistance genotypes and phenotypes. Herein, a novel method that utilizes artificial intelligence to analyze Raman spectra to identify microbes and their susceptibility to commonly used antibiotics at both genotype and phenotype level is developed. A total of 130 strains of <jats:italic>Enterococcus<\/jats:italic> spp. and <jats:italic>Staphylococcus capitis<\/jats:italic> with known minimum inhibitory concentrations (MICs) of commonly used antimicrobial agents are included in this study. After the models are configured and trained, long short\u2010term memory (LSTM) based Raman platform is developed and is found to be able to offer an accuracy of 89.9\u2009\u00b1\u20091.1%, 82.4\u2009\u00b1\u20090.6%, and 60.4\u201389.2% in bacterial species classification, identification of antimicrobial\u2010resistance genes (ARGs), and prediction of resistance phenotypes, respectively. This novel method exhibits higher level of accuracy than those using the machine learning algorithms. The results indicate that Raman spectroscopy combined with LSTM analysis can be used for rapid bacterial species identification, detection of ARGs, and assessment of drug\u2010resistance phenotypes.<\/jats:p><\/jats:sec>","DOI":"10.1002\/aisy.202200235","type":"journal-article","created":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T20:21:52Z","timestamp":1677097312000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Rapid Identification of Species, Antimicrobial\u2010Resistance Genotypes and Phenotypes of Gram\u2010Positive Cocci Using Long Short\u2010Term Memory Raman Spectra Methods"],"prefix":"10.1002","volume":"5","author":[{"given":"Jiayue","family":"Lu","sequence":"first","affiliation":[{"name":"Department of Clinical Laboratory Second Affiliated Hospital, Zhejiang University School of Medicine  Hangzhou 310000 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