ArticleJournal of clinical microbiology2016
Automatic Digital Analysis of Chromogenic Media for Vancomycin-Resistant-Enterococcus Screens Using Copan WASPLab.
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 29 papers.
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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
29 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
- Proceedings of the Clinical Microbiology Open 2024: artificial intelligence applications in clinical microbiology.Journal of clinical microbiology · 2025Article
- Evaluation of an automatic image classifier for analysis of bacterial growth on a multiple-agar plate developed for bovine mastitis.PloS one · 2025Article
- Development and evaluation of an artificial intelligence for bacterial growth monitoring in clinical bacteriology.Journal of clinical microbiology · 2024Article
- Automated detection of methicillin-resistantJournal 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
- Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology.Nature communications · 2023Article
- The Use of Machine Learning for Image Analysis Artificial Intelligence in Clinical Microbiology.Journal of clinical microbiology · 2023Review
- Laboratory automation, informatics, and artificial intelligence: current and future perspectives in clinical microbiology.Frontiers in cellular and infection microbiology · 2023Article
- Total Laboratory Automation for Rapid Detection and Identification of Microorganisms and Their Antimicrobial Resistance Profiles.Frontiers in cellular and infection microbiology · 2022Review
- Microbiology 2.0-A "behind the scenes" consideration for artificial intelligence applications for interpretive culture plate reading in routine diagnostic laboratories.Frontiers in microbiology · 2022Article
- 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
- Evaluation of the WASPLab Segregation Software To Automatically Analyze Urine Cultures Using Routine Blood and MacConkey Agars.Journal of clinical microbiology · 2020Article
- Machine Learning Takes Laboratory Automation to the Next Level.Journal of clinical microbiology · 2020Article
- Evaluation of WASPLab Software To Automatically Read chromID CPS Elite Agar for Reporting of Urine Cultures.Journal of clinical microbiology · 2019Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Vancomycin-resistant enterococci (VRE) are an important cause of health care-acquired infections (HAIs). Studies have shown that active surveillance of high-risk patients for VRE colonization can aid in reducing HAIs; however, these screens generate a significant cost to the laboratory and health care system. Digital imaging capable of differentiating negative and "nonnegative" chromogenic agar can reduce the labor cost of these screens and potentially improve patient care. In this study, we evaluated the performance of the WASPLab Chromogenic Detection Module (CDM) (Copan, Brescia, Italy) software to analyze VRE chromogenic agar and compared the results to technologist plate reading. Specimens collected at 3 laboratories were cultured using the WASPLab CDM and plated to each site's standard-of-care chromogenic media, which included Colorex VRE (BioMed Diagnostics, White City, OR) or Oxoid VRE (Oxoid, Basingstoke, United Kingdom). Digital images were scored using the CDM software after 24 or 40 h of growth, and all manual reading was performed using digital images on a high-definition (HD) monitor. In total, 104,730 specimens were enrolled and automation agreed with manual analysis for 90.1% of all specimens tested, with sensitivity and specificity of 100% and 89.5%, respectively. Automation results were discordant for 10,348 specimens, and all discordant images were reviewed by a laboratory supervisor or director. After a second review, 499 specimens were identified as representing missed positive cultures falsely called negative by the technologist, 1,616 were identified as containing borderline color results (negative result but with no package insert color visible), and 8,234 specimens were identified as containing colorimetric pigmentation due to residual matrix from the specimen or yeast (Candida). Overall, the CDM was accurate at identifying negative VRE plates, which comprised 84% (87,973) of the specimens in this study.
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