Detection and Identification of the Stages of DH5-Alpha Escherichia coli Biofilm Formation on Metal by Using an Artificial Intelligence System

In clinical environments, orthopedic implants are associated with a risk of infection during implantation. However, the growth paths of bacteria on metal, which is nontransparent, are difficult to observe. In this study, we visualized the DH5-alpha Escherichia coli bacterial growth path on the surfa...

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Bibliographic Details
Published in:Microscopy and microanalysis 2021-10, Vol.27 (5), p.1218-1225
Main Authors: Wong, Pei-Chun, Fan, Tai-En, Lee, Yu-Lin, Lai, Chun-Yu, Wu, Jia-Lin, Chang, Ling-Hua, Su, Tai-Yuan
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial neural networks
Bacteria
Biofilms
Biomedical materials
Deep learning
E coli
Escherichia coli
Growth stage
Health risks
Magnesium
Metal surfaces
Morphology
Neural networks
Orthopaedic implants
Orthopedics
Scanning electron microscopy
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Summary:In clinical environments, orthopedic implants are associated with a risk of infection during implantation. However, the growth paths of bacteria on metal, which is nontransparent, are difficult to observe. In this study, we visualized the DH5-alpha Escherichia coli bacterial growth path on the surface of magnesium by using scanning electron microscope (SEM) images and constructed a convolutional neural network-based artificial intelligence (AI) system to identify metal surfaces, bacteria, and its generated products to grade the growth stage of the bacteria implanted on the magnesium. The detection result of the E. coli growth stage by the AI system was close to that manually marked by experts, and it may greatly accelerate the investigation of the bacterial growth process in various types of metallic material.
ISSN:1431-9276
1435-8115
DOI:10.1017/S1431927621012472