Machine Learning Takes Laboratory Automation to the Next Level.
Abstract
Clinical microbiology laboratories face challenges with workload and understaffing that other clinical laboratory sections have addressed with automation. In this issue of the , Faron and colleagues (J Clin Microbiol 58:e01683-19, 2019, 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 colony forming units. 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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JCM.00012-20
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