ArticleBiomedical journal2017
Towards automated detection, semi-quantification and identification of microbial growth in clinical bacteriology: A proof of concept.
Article in Biomedical journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Evaluation of the BD urine culture application for artificial intelligence-assisted urine culture interpretation.Journal of clinical microbiology · 2026Article
- The Impact of Laboratory Automation on the Time to Urine Microbiological Results: A Five-Year Retrospective Study.Diagnostics (Basel, Switzerland) · 2024Article
- Development and evaluation of an artificial intelligence for bacterial growth monitoring in clinical bacteriology.Journal of clinical microbiology · 2024Article
- Rapid and automated screening of carbapenemase- and ESBL-producing Gram-negative bacteria from rectal swabs using chromogenic agar media and the ScanStation device.Microbiology spectrum · 2023Article
- Machine learning algorithms in microbial classification: a comparative analysis.Frontiers in artificial intelligence · 2023Review
- Laboratory automation, informatics, and artificial intelligence: current and future perspectives in clinical microbiology.Frontiers in cellular and infection microbiology · 2023Article
- Rapid, Low-Complexity, Simultaneous Bacterial Group Identification and Antimicrobial Susceptibility Testing Performed Directly on Positive Blood Culture Bottles Using Chromogenic Agar.The American journal of tropical medicine and hygiene · 2022Article
- Molecular epidemiology of bovine tuberculosis in Northern Ghana identifies several uncharacterized bovine spoligotypes and suggests possible zoonotic transmission.PLoS neglected tropical diseases · 2022Article
- Digital imaging for reading of direct rapid antibiotic susceptibility tests from positive blood cultures.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2021Article
- State-of-the-Art Techniques for Diagnosis of Medical Parasites and Arthropods.Diagnostics (Basel, Switzerland) · 2021Review
- Benefits Derived from Full Laboratory Automation in Microbiology: a Tale of Four Laboratories.Journal of clinical microbiology · 2021Article
- Digitalization, clinical microbiology and infectious diseases.Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases · 2020Article
- Digital microbiology.Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases · 2020Review
- Consolidation of Clinical Microbiology Laboratories and Introduction of Transformative Technologies.Clinical microbiology reviews · 2020Review
- Laboratory Automation in Clinical Microbiology.Bioengineering (Basel, Switzerland) · 2018Review
- Significant increase in cultivation of Gardnerella vaginalis, Alloscardovia omnicolens, Actinotignum schaalii, and Actinomyces spp. in urine samples with total laboratory automation.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2018Article
- Microbes are off the menu: Defective macrophage phagocytosis in COPD.Biomedical journal · 2017Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAutomation in microbiology laboratories impacts management, workflow, productivity and quality. Further improvements will be driven by the development of intelligent image analysis allowing automated detection of microbial growth, release of sterile samples, identification and quantification of bacterial colonies and reading of AST disk diffusion assays. We investigated the potential benefit of intelligent imaging analysis by developing algorithms allowing automated detection, semi-quantification and identification of bacterial colonies.
methodsDefined monomicrobial and clinical urine samples were inoculated by the BD Kiestra™ InoqulA™ BT module. Image acquisition of plates was performed with the BD Kiestra™ ImagA BT digital imaging module using the BD Kiestra™ Optis™ imaging software. The algorithms were developed and trained using defined data sets and their performance evaluated on both defined and clinical samples.
resultsThe detection algorithms exhibited 97.1% sensitivity and 93.6% specificity for microbial growth detection. Moreover, quantification accuracy of 80.2% and of 98.6% when accepting a 1 log tolerance was obtained with both defined monomicrobial and clinical urine samples, despite the presence of multiple species in the clinical samples. Automated identification accuracy of microbial colonies growing on chromogenic agar from defined isolates or clinical urine samples ranged from 98.3% to 99.7%, depending on the bacterial species tested.
conclusionThe development of intelligent algorithm represents a major innovation that has the potential to significantly increase laboratory quality and productivity while reducing turn-around-times. Further development and validation with larger numbers of defined and clinical samples should be performed before transferring intelligent imaging analysis into diagnostic laboratories.
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