ArticleFrontiers in microbiology2022
ScanGrow: Deep Learning-Based Live Tracking of Bacterial Growth in Broth.
Article in Frontiers in microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Kineochelins-A New Group of Siderophores From an Antarctic Bacterium.Microbial biotechnology · 2026Article
- Monoclonal Antibodies and Derivatives: Therapeutic Tools for Cancer.Oncology research · 2026Review
- The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance.Computational and structural biotechnology journal · 2025Review
- Advancing Monoclonal Antibody Manufacturing: Process Optimization, Cost Reduction Strategies, and Emerging Technologies.Biologics : targets & therapy · 2025Review
- Article
- Establishing Compliance between Spectral, Colourimetric and Photometric Indicators in Resazurin Reduction Test.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.