Rapid Identification of Species, Antimicrobial‐Resistance Genotypes and Phenotypes of Gram‐Positive Cocci Using Long Short‐Term Memory Raman Spectra Methods

Antimicrobial resistance is an aggravating public health problem worldwide, with more than 700 000 deaths attributable to infections caused by antibiotic‐resistant bacteria annually. 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‐resistance 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 Enterococcus spp. and Staphylococcus capitis 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‐term memory (LSTM) based Raman platform is developed and is found to be able to offer an accuracy of 89.9 ± 1.1%, 82.4 ± 0.6%, and 60.4–89.2% in bacterial species classification, identification of antimicrobial‐resistance 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‐resistance phenotypes. Herein, a novel method that used artificial intelligence to analyze biological Raman spectra to identify microbes and their antimicrobial resistance at both genotype and phenotype levels is developed. The novel method exhibits higher level of accuracy than those obtained using the machine learning algorithms. This technique is a very promising tool with very high clinical application potential.