ArticleJournal of clinical microbiology2020
Evaluation of the WASPLab Segregation Software To Automatically Analyze Urine Cultures Using Routine Blood and MacConkey Agars.
Article in Journal of clinical microbiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Improving turnaround times with artificial intelligence in microbiology.Journal of clinical microbiology · 2026Article
- Evaluation of the BD urine culture application for artificial intelligence-assisted urine culture interpretation.Journal of clinical microbiology · 2026Article
- Non-destructive label-free automated identification of bacterial colonies at the species level directly on agar media using digital holography and convolutional neural network algorithms.Microbiology spectrum · 2026Article
- Evaluation of an AI-Assisted Colony Counting System Across Multiple Culture Media Using Standardized Pure Culture Plates.Microorganisms · 2026Article
- Evolving microbiology laboratories: mastering automated culture-based processes and molecular assays, an institutional experience.Frontiers in cellular and infection microbiology · 2026Review
- From microscopy to antimicrobial decisions: a clinically grounded roadmap for critical care infectious diseases.Frontiers in artificial intelligence · 2026Review
- Implementation of convolutional neural networks for microbial colony recognition.Microbiology spectrum · 2025Article
- Evaluation of automated streaking patterns in urine culture for clinical workflow optimization.Scientific reports · 2025Article
- Conversion of a classical microbiology laboratory to a total automation laboratory enhanced by the application of lean principles.Microbiology spectrum · 2024Article
- The Use of Machine Learning for Image Analysis Artificial Intelligence in Clinical Microbiology.Journal of clinical microbiology · 2023Review
- Comparison of the APAS Independence Automated Plate Reader System with the Manual Standard of Care for Processing Urine Culture Specimens.Microbiology spectrum · 2022Article
- Total Laboratory Automation for Rapid Detection and Identification of Microorganisms and Their Antimicrobial Resistance Profiles.Frontiers in cellular and infection microbiology · 2022Review
- Benefits Derived from Full Laboratory Automation in Microbiology: a Tale of Four Laboratories.Journal of clinical microbiology · 2021Article
- Copan Walk Away Specimen Processor (WASP) Automated System for Pathogen Detection in Female Reproductive Tract Specimens.Frontiers in cellular and infection microbiology · 2021Article
- Machine Learning Takes Laboratory Automation to the Next Level.Journal of clinical microbiology · 2020Article
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5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automation of the clinical microbiology laboratory has become more prominent as laboratories face higher specimen volumes and understaffing and are becoming more consolidated. One recent advancement is the use of digital image analysis to rapidly distinguish between chromogenic growth for screening bacterial cultures. In this study, colony segregation software developed by Copan (Brescia, Italy) was evaluated to distinguish between significant growth and no growth of urine cultures plated onto standard blood and MacConkey agars. Specimens from 3 sites were processed on a WASP instrument (Copan) and incubated on the WASPLab platform (Copan), and plates were imaged at 0 and 24 hours postinoculation. Images were read by technologists following validated laboratory protocols (VLPs), and results were recorded in the laboratory information systems (LIS). Image analysis performed colony counts on the 24-hour images, and results were compared with the VLP. A total of 12,931 urine cultures were tested and analyzed with an overall sensitivity and specificity of 99.8% and 72.0%, respectively. After secondary review, 91.1% of manual-positive/automation-negative specimens were due to expert rules that reported the plate as contaminated or growing only normal flora and not due to threshold counts. Nine specimens were found to be manual-positive/automation-negative; a secondary review demonstrated that the results of 8 of these specimens were due to growth of microcolonies that were programmed to be ignored by the software and 1 were due to a colony count near the limit of significance. Overall, the image analysis software proved to be highly sensitive and can be utilized by laboratories to batch-review negative cultures to improve laboratory workflow.
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