TY - JOUR
T1 - Machine learning takes laboratory automation to the next level
AU - Ford, Bradley A.
AU - McElvania, Erin
N1 - Publisher Copyright:
© 2020 American Society for Microbiology. All Rights Reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
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U2 - 10.1128/JCM.00012-20
DO - 10.1128/JCM.00012-20
M3 - Article
C2 - 32024725
AN - SCOPUS:85082542352
SN - 0095-1137
VL - 58
JO - Journal of clinical microbiology
JF - Journal of clinical microbiology
IS - 4
M1 - e00012-20
ER -