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迷你综述:自动化和数字健康时代的临床常规微生物学。

Mini Review: Clinical Routine Microbiology in the Era of Automation and Digital Health.

机构信息

Genomic Research Laboratory, Division of Infectious Diseases, Department of Medicine, Geneva University Hospitals and University of Geneva, Geneva, Switzerland.

Bacteriology Laboratory, Division of Laboratory Medicine, Department of Diagnostics, Geneva University Hospitals, Geneva, Switzerland.

出版信息

Front Cell Infect Microbiol. 2020 Nov 30;10:582028. doi: 10.3389/fcimb.2020.582028. eCollection 2020.

Abstract

Clinical microbiology laboratories are the first line to combat and handle infectious diseases and antibiotic resistance, including newly emerging ones. Although most clinical laboratories still rely on conventional methods, a cascade of technological changes, driven by digital imaging and high-throughput sequencing, will revolutionize the management of clinical diagnostics for direct detection of bacteria and swift antimicrobial susceptibility testing. Importantly, such technological advancements occur in the golden age of machine learning where computers are no longer acting passively in data mining, but once trained, can also help physicians in making decisions for diagnostics and optimal treatment administration. The further potential of physically integrating new technologies in an automation chain, combined to machine-learning-based software for data analyses, is seducing and would indeed lead to a faster management in infectious diseases. However, if, from one side, technological advancement would achieve a better performance than conventional methods, on the other side, this evolution challenges clinicians in terms of data interpretation and impacts the entire hospital personnel organization and management. In this mini review, we discuss such technological achievements offering practical examples of their operability but also their limitations and potential issues that their implementation could rise in clinical microbiology laboratories.

摘要

临床微生物学实验室是抗击和处理传染病和抗生素耐药性的第一线,包括新出现的传染病和抗生素耐药性。虽然大多数临床实验室仍然依赖传统方法,但一系列由数字成像和高通量测序驱动的技术变革,将彻底改变直接检测细菌和快速抗菌药物敏感性测试的临床诊断管理。重要的是,这种技术进步发生在机器学习的黄金时代,计算机不再被动地进行数据挖掘,而且一旦经过训练,还可以帮助医生做出诊断和最佳治疗管理的决策。将新技术在自动化链中进行物理集成,并结合基于机器学习的数据分析软件,具有很大的吸引力,确实可以加快传染病的管理。然而,如果一方面技术进步能够比传统方法取得更好的效果,另一方面,这种发展变化在数据解释方面对临床医生提出了挑战,并影响到整个医院人员的组织和管理。在这篇小型综述中,我们讨论了这些技术成就,提供了其实用性的实际例子,但也讨论了它们的局限性和潜在问题,以及它们在临床微生物学实验室中的实施可能带来的问题。

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