Cargando…
Rapid Bacterial Detection in Urine Using Laser Scattering and Deep Learning Analysis
Images of laser scattering patterns generated by bacteria in urine are promising resources for deep learning. However, floating bacteria in urine produce dynamic scattering patterns and require deep learning of spatial and temporal features. We hypothesized that bacteria with variable bacterial dens...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society for Microbiology
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8941854/ https://www.ncbi.nlm.nih.gov/pubmed/35234514 http://dx.doi.org/10.1128/spectrum.01769-21 |
_version_ | 1784673189826134016 |
---|---|
author | Lee, Kwang Seob Lim, Hyung Jae Kim, Kyungnam Park, Yeon-Gyeong Yoo, Jae-Woo Yong, Dongeun |
author_facet | Lee, Kwang Seob Lim, Hyung Jae Kim, Kyungnam Park, Yeon-Gyeong Yoo, Jae-Woo Yong, Dongeun |
author_sort | Lee, Kwang Seob |
collection | PubMed |
description | Images of laser scattering patterns generated by bacteria in urine are promising resources for deep learning. However, floating bacteria in urine produce dynamic scattering patterns and require deep learning of spatial and temporal features. We hypothesized that bacteria with variable bacterial densities and different Gram staining reactions would generate different speckle images. After deep learning of speckle patterns generated by various densities of bacteria in artificial urine, we validated the model in an independent set of clinical urine samples in a tertiary hospital. Even at a low bacterial density cutoff (1,000 CFU/mL), the model achieved a predictive accuracy of 90.9% for positive urine culture. At a cutoff of 50,000 CFU/mL, it showed a better accuracy of 98.5%. The model achieved satisfactory accuracy at both cutoff levels for predicting the Gram staining reaction. Considering only 30 min of analysis, our method appears as a new screening tool for predicting the presence of bacteria before urine culture. IMPORTANCE This study performed deep learning of multiple laser scattering patterns by the bacteria in urine to predict positive urine culture. Conventional urine analyzers have limited performance in identifying bacteria in urine. This novel method showed a satisfactory accuracy taking only 30 min of analysis without conventional urine culture. It was also developed to predict the Gram staining reaction of the bacteria. It can be used as a standalone screening tool for urinary tract infection. |
format | Online Article Text |
id | pubmed-8941854 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Society for Microbiology |
record_format | MEDLINE/PubMed |
spelling | pubmed-89418542022-03-24 Rapid Bacterial Detection in Urine Using Laser Scattering and Deep Learning Analysis Lee, Kwang Seob Lim, Hyung Jae Kim, Kyungnam Park, Yeon-Gyeong Yoo, Jae-Woo Yong, Dongeun Microbiol Spectr Research Article Images of laser scattering patterns generated by bacteria in urine are promising resources for deep learning. However, floating bacteria in urine produce dynamic scattering patterns and require deep learning of spatial and temporal features. We hypothesized that bacteria with variable bacterial densities and different Gram staining reactions would generate different speckle images. After deep learning of speckle patterns generated by various densities of bacteria in artificial urine, we validated the model in an independent set of clinical urine samples in a tertiary hospital. Even at a low bacterial density cutoff (1,000 CFU/mL), the model achieved a predictive accuracy of 90.9% for positive urine culture. At a cutoff of 50,000 CFU/mL, it showed a better accuracy of 98.5%. The model achieved satisfactory accuracy at both cutoff levels for predicting the Gram staining reaction. Considering only 30 min of analysis, our method appears as a new screening tool for predicting the presence of bacteria before urine culture. IMPORTANCE This study performed deep learning of multiple laser scattering patterns by the bacteria in urine to predict positive urine culture. Conventional urine analyzers have limited performance in identifying bacteria in urine. This novel method showed a satisfactory accuracy taking only 30 min of analysis without conventional urine culture. It was also developed to predict the Gram staining reaction of the bacteria. It can be used as a standalone screening tool for urinary tract infection. American Society for Microbiology 2022-03-02 /pmc/articles/PMC8941854/ /pubmed/35234514 http://dx.doi.org/10.1128/spectrum.01769-21 Text en Copyright © 2022 Lee et al. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Article Lee, Kwang Seob Lim, Hyung Jae Kim, Kyungnam Park, Yeon-Gyeong Yoo, Jae-Woo Yong, Dongeun Rapid Bacterial Detection in Urine Using Laser Scattering and Deep Learning Analysis |
title | Rapid Bacterial Detection in Urine Using Laser Scattering and Deep Learning Analysis |
title_full | Rapid Bacterial Detection in Urine Using Laser Scattering and Deep Learning Analysis |
title_fullStr | Rapid Bacterial Detection in Urine Using Laser Scattering and Deep Learning Analysis |
title_full_unstemmed | Rapid Bacterial Detection in Urine Using Laser Scattering and Deep Learning Analysis |
title_short | Rapid Bacterial Detection in Urine Using Laser Scattering and Deep Learning Analysis |
title_sort | rapid bacterial detection in urine using laser scattering and deep learning analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8941854/ https://www.ncbi.nlm.nih.gov/pubmed/35234514 http://dx.doi.org/10.1128/spectrum.01769-21 |
work_keys_str_mv | AT leekwangseob rapidbacterialdetectioninurineusinglaserscatteringanddeeplearninganalysis AT limhyungjae rapidbacterialdetectioninurineusinglaserscatteringanddeeplearninganalysis AT kimkyungnam rapidbacterialdetectioninurineusinglaserscatteringanddeeplearninganalysis AT parkyeongyeong rapidbacterialdetectioninurineusinglaserscatteringanddeeplearninganalysis AT yoojaewoo rapidbacterialdetectioninurineusinglaserscatteringanddeeplearninganalysis AT yongdongeun rapidbacterialdetectioninurineusinglaserscatteringanddeeplearninganalysis |