Open AccessMachine Learning Takes Laboratory Automation to the Next Level
Author(s) -
Bradley Ford,
Erin McElvania
Publication year - 2020
Publication title -
journal of clinical microbiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.349
H-Index - 255
eISSN - 1070-633X
pISSN - 0095-1137
DOI - 10.1128/jcm.00012-20
Subject(s) - automation , laboratory automation , computer science , data science , engineering , mechanical engineering
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.