机器学习将实验室自动化提升到新的水平。
Machine Learning Takes Laboratory Automation to the Next Level.
机构信息
Department of Pathology, University of Iowa Hospitals and Clinics, Iowa City, Iowa, USA.
Department of Pathology and Laboratory Medicine, NorthShore University HealthSystem, Evanston, Illinois, USA
出版信息
J Clin Microbiol. 2020 Mar 25;58(4). doi: 10.1128/JCM.00012-20.
Abstract
Clinical microbiology laboratories face challenges with workload and understaffing that other clinical laboratory sections have addressed with automation. In this issue of the , 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.
摘要
临床微生物学实验室面临着工作量大、人手不足的挑战，而其他临床实验室部门已经通过自动化解决了这些问题。在本期的《临床微生物学杂志》中，M. L. Faron、B. W. Buchan、R. F. Relich、J. Clark 和 N. A. Ledeboer（J Clin Microbiol 58:e01683-19, 2020, https://doi.org/10.1128/JCM.01683-19）评估了自动化图像分析软件根据尿液培养物的总 CFU 数量进行进一步检测的性能。尿液培养物是大多数实验室中数量最大的标本类型，因此由于菌落定量的一致性，该软件有可能极大地提高实验室效率和质量。
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