Machine Learning Takes Laboratory Automation to the Next Level

Bradley A Ford; Erin McElvania

doi:10.1128/JCM.00012-20

Machine Learning Takes Laboratory Automation to the Next Level

J Clin Microbiol. 2020 Mar 25;58(4):e00012-20. doi: 10.1128/JCM.00012-20. Print 2020 Mar 25.

Authors

Bradley A Ford¹, Erin McElvania²

Affiliations

¹ Department of Pathology, University of Iowa Hospitals and Clinics, Iowa City, Iowa, USA.
² Department of Pathology and Laboratory Medicine, NorthShore University HealthSystem, Evanston, Illinois, USA emcelvania@northshore.org.

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.

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MeSH terms

Automation, Laboratory*
Clinical Laboratory Services*
Laboratories
Machine Learning
Software