ArticleJournal of clinical microbiology2016
Automated Scoring of Chromogenic Media for Detection of Methicillin-Resistant Staphylococcus aureus by Use of WASPLab Image Analysis Software.
Article in Journal of clinical microbiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Improving turnaround times with artificial intelligence in microbiology.Journal of clinical microbiology · 2026Article
- Validation of Digital Slide Scanning and a Convolutional Neural Network for the Detection of Intestinal Parasites in Human Stool Samples.Diagnostics (Basel, Switzerland) · 2025Article
- Implementation of convolutional neural networks for microbial colony recognition.Microbiology spectrum · 2025Article
- Proceedings of the Clinical Microbiology Open 2024: artificial intelligence applications in clinical microbiology.Journal of clinical microbiology · 2025Article
- Automated detection of methicillin-resistantJournal of clinical microbiology · 2024Article
- Development and evaluation of an artificial intelligence for bacterial growth monitoring in clinical bacteriology.Journal of clinical microbiology · 2024Article
- Evaluation of PhenoMATRIX and PhenoMATRIX PLUS for the screening of MRSA from nasal and inguinal/perineal swabs using chromogenic media.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
- The Use of Machine Learning for Image Analysis Artificial Intelligence in Clinical Microbiology.Journal of clinical microbiology · 2023Review
- Recent Advances in Nanozyme-Mediated Strategies for Pathogen Detection and Control.International journal of molecular sciences · 2023Review
- Laboratory automation, informatics, and artificial intelligence: current and future perspectives in clinical microbiology.Frontiers in cellular and infection microbiology · 2023Article
- Outbreak investigation including molecular characterization of community associated methicillin-resistant Staphylococcus aureus in a primary and secondary school in Eastern Switzerland.Scientific reports · 2022Article
- Application of Nanomaterials in the Prevention, Detection, and Treatment of Methicillin-ResistantPharmaceutics · 2022Review
- Total Laboratory Automation for Rapid Detection and Identification of Microorganisms and Their Antimicrobial Resistance Profiles.Frontiers in cellular and infection microbiology · 2022Review
- Comparison of an Automated Plate Assessment System (APAS Independence) and Artificial Intelligence (AI) to Manual Plate Reading of Methicillin-Resistant and Methicillin-Susceptible Staphylococcus aureus CHROMagar Surveillance Cultures.Journal of clinical microbiology · 2021Article
- Benefits Derived from Full Laboratory Automation in Microbiology: a Tale of Four Laboratories.Journal of clinical microbiology · 2021Article
- Laboratory Automation in the Microbiology Laboratory: an Ongoing Journey, Not a Tale?Journal of clinical microbiology · 2021Article
- Image analysis and artificial intelligence in infectious disease diagnostics.Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases · 2020Review
- Computer Vision and Artificial Intelligence Are Emerging Diagnostic Tools for the Clinical Microbiologist.Journal of clinical microbiology · 2020Article
- Machine Learning Takes Laboratory Automation to the Next Level.Journal of clinical microbiology · 2020Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, systems have been developed to create total laboratory automation for clinical microbiology. These systems allow for the automation of specimen processing, specimen incubation, and imaging of bacterial growth. In this study, we used the WASPLab to validate software that discriminates and segregates positive and negative chromogenic methicillin-resistant Staphylococcus aureus (MRSA) plates by recognition of pigmented colonies. A total of 57,690 swabs submitted for MRSA screening were enrolled in the study. Four sites enrolled specimens following their standard of care. Chromogenic agar used at these sites included MRSASelect (Bio-Rad Laboratories, Redmond, WA), chromID MRSA (bioMérieux, Marcy l'Etoile, France), and CHROMagar MRSA (BD Diagnostics, Sparks, MD). Specimens were plated and incubated using the WASPLab. The digital camera took images at 0 and 16 to 24 h and the WASPLab software determined the presence of positive colonies based on a hue, saturation, and value (HSV) score. If the HSV score fell within a defined threshold, the plate was called positive. The performance of the digital analysis was compared to manual reading. Overall, the digital software had a sensitivity of 100% and a specificity of 90.7% with the specificity ranging between 90.0 and 96.0 across all sites. The results were similar using the three different agars with a sensitivity of 100% and specificity ranging between 90.7 and 92.4%. These data demonstrate that automated digital analysis can be used to accurately sort positive from negative chromogenic agar cultures regardless of the pigmentation produced.
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