Rapid identification of the resistance of urinary tract pathogenic bacteria using deep learning–based spectroscopic analysis
- 21 October 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in Analytical and Bioanalytical Chemistry
- Vol. 413 (30), 7401-7410
- https://doi.org/10.1007/s00216-021-03691-z
Abstract
The resistance of urinary tract pathogenic bacteria to various antibiotics is increasing, which requires the rapid detection of infectious pathogens for accurate and timely antibiotic treatment. Here, we propose a rapid diagnosis strategy for the antibiotic resistance of bacteria in urinary tract infections (UTIs) based on surface-enhanced Raman scattering (SERS) using a positively charged gold nanoparticle planar solid SERS substrate. Then, an intelligent identification model for SERS spectra based on the deep learning technique is constructed to realize the rapid, ultrasensitive, and non-labeled detection of pathogenic bacteria. A total of 54,000 SERS spectra were collected from 18 isolates belonging to 6 species of common UTI bacteria in this work to realize identification of bacterial species, antibiotic sensitivity, and multidrug resistance (MDR) via convolutional neural networks (CNN). This method significantly simplify the Raman data processing processes without background removing and smoothing, however, achieving 96% above classification accuracy, which was significantly greater than the 85% accuracy of the traditional multivariate statistical analysis algorithm principal component analysis combined with the K-nearest neighbor (PCA-KNN). This work clearly elucidated the potential of combining SERS and deep learning technique to realize culture-free identification of pathogenic bacteria and their associated antibiotic sensitivity.Keywords
Funding Information
- The Discipline Construction Project of Guangdong Medical University (4SG21022G)
This publication has 48 references indexed in Scilit:
- Prevalence of Inappropriate Antibiotic Prescriptions Among US Ambulatory Care Visits, 2010–2011Obstetrical & Gynecological Survey, 2016
- Three-dimensional SERS hot spots for chemical sensing: Towards developing a practical analyzerTrAC Trends in Analytical Chemistry, 2016
- Prevalence of Inappropriate Antibiotic Prescriptions Among US Ambulatory Care Visits, 2010-2011JAMA, 2016
- Prospects for point-of-care pathogen diagnostics using surface-enhanced Raman scattering (SERS)Chemical Society Reviews, 2016
- Detection of vancomycin resistances in enterococci within 3 ½ hoursScientific Reports, 2015
- Towards a receptor-free immobilization and SERS detection of urinary tract infections causative pathogensAnalytical and Bioanalytical Chemistry, 2014
- Urinary Tract Infection SyndromesInfectious Disease Clinics of North America, 2014
- Next-Generation Antimicrobial Susceptibility TestingJournal of Clinical Microbiology, 2013
- Methods of InvestigationDeutsches Ärzteblatt international, 2010
- Automated Autofluorescence Background Subtraction Algorithm for Biomedical Raman SpectroscopyApplied Spectroscopy, 2007