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ScanGrow: Deep Learning-Based Live Tracking of Bacterial Growth in Broth
Monitoring the growth of bacterial cultures is one of the most common techniques in microbiology. This is usually achieved by using expensive and bulky spectrophotometric plate readers which periodically measure the optical density of bacterial cultures during the incubation period. In this study, w...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9343779/ https://www.ncbi.nlm.nih.gov/pubmed/35928161 http://dx.doi.org/10.3389/fmicb.2022.900596 |
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author | Worth, Ross Michael Espina, Laura |
author_facet | Worth, Ross Michael Espina, Laura |
author_sort | Worth, Ross Michael |
collection | PubMed |
description | Monitoring the growth of bacterial cultures is one of the most common techniques in microbiology. This is usually achieved by using expensive and bulky spectrophotometric plate readers which periodically measure the optical density of bacterial cultures during the incubation period. In this study, we present a completely novel way of obtaining bacterial growth curves based on the classification of scanned images of cultures rather than using spectrophotometric measurements. We trained a deep learning model with images of bacterial broths contained in microplates, and we integrated it into a custom-made software application that triggers a flatbed scanner to timely capture images, automatically processes the images, and represents all growth curves. The developed tool, ScanGrow, is presented as a low-cost and high-throughput alternative to plate readers, and it only requires a computer connected to a flatbed scanner and equipped with our open-source ScanGrow application. In addition, this application also assists in the pre-processing of data to create and evaluate new models, having the potential to facilitate many routine microbiological techniques. |
format | Online Article Text |
id | pubmed-9343779 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93437792022-08-03 ScanGrow: Deep Learning-Based Live Tracking of Bacterial Growth in Broth Worth, Ross Michael Espina, Laura Front Microbiol Microbiology Monitoring the growth of bacterial cultures is one of the most common techniques in microbiology. This is usually achieved by using expensive and bulky spectrophotometric plate readers which periodically measure the optical density of bacterial cultures during the incubation period. In this study, we present a completely novel way of obtaining bacterial growth curves based on the classification of scanned images of cultures rather than using spectrophotometric measurements. We trained a deep learning model with images of bacterial broths contained in microplates, and we integrated it into a custom-made software application that triggers a flatbed scanner to timely capture images, automatically processes the images, and represents all growth curves. The developed tool, ScanGrow, is presented as a low-cost and high-throughput alternative to plate readers, and it only requires a computer connected to a flatbed scanner and equipped with our open-source ScanGrow application. In addition, this application also assists in the pre-processing of data to create and evaluate new models, having the potential to facilitate many routine microbiological techniques. Frontiers Media S.A. 2022-07-19 /pmc/articles/PMC9343779/ /pubmed/35928161 http://dx.doi.org/10.3389/fmicb.2022.900596 Text en Copyright © 2022 Worth and Espina. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Microbiology Worth, Ross Michael Espina, Laura ScanGrow: Deep Learning-Based Live Tracking of Bacterial Growth in Broth |
title | ScanGrow: Deep Learning-Based Live Tracking of Bacterial Growth in Broth |
title_full | ScanGrow: Deep Learning-Based Live Tracking of Bacterial Growth in Broth |
title_fullStr | ScanGrow: Deep Learning-Based Live Tracking of Bacterial Growth in Broth |
title_full_unstemmed | ScanGrow: Deep Learning-Based Live Tracking of Bacterial Growth in Broth |
title_short | ScanGrow: Deep Learning-Based Live Tracking of Bacterial Growth in Broth |
title_sort | scangrow: deep learning-based live tracking of bacterial growth in broth |
topic | Microbiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9343779/ https://www.ncbi.nlm.nih.gov/pubmed/35928161 http://dx.doi.org/10.3389/fmicb.2022.900596 |
work_keys_str_mv | AT worthrossmichael scangrowdeeplearningbasedlivetrackingofbacterialgrowthinbroth AT espinalaura scangrowdeeplearningbasedlivetrackingofbacterialgrowthinbroth |