Machine learning takes laboratory automation to the next level

Bradley A. Ford, Erin McElvania

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Clinical microbiology laboratories face challenges with workload and understaffing that other clinical laboratory sections have addressed with automation. In this issue of the Journal of Clinical Microbiology, M. L. Faron, B. W. Buchan, R. F. Relich, J. Clark, and N. A. Ledeboer (J Clin Microbiol 58:e01683-19, 2020, https://doi.org/10.1128/JCM.01683-19) evaluate the performance of automated image analysis software to screen urine cultures for further workup according to their total number of CFU. Urine cultures are the highest volume specimen type for most laboratories, so this software has the potential for tremendous gains in laboratory efficiency and quality due to the consistency of colony quantification.

Original languageEnglish (US)
Article numbere00012-20
JournalJournal of clinical microbiology
Volume58
Issue number4
DOIs
StatePublished - 2020
Externally publishedYes

ASJC Scopus subject areas

  • Microbiology (medical)

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