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Characterizing Microbiome Dynamics &#8211; Flow Cytometry Based Workflows from Pure Cultures to Natural Communities
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Digital microbiology.

A Egli1, J Schrenzel2, G Greub3

  • 1Clinical Bacteriology and Mycology, University Hospital Basel, Basel, Switzerland; Applied Microbiology Research, Department of Biomedicine, University of Basel, Basel, Switzerland.

Clinical Microbiology and Infection : the Official Publication of the European Society of Clinical Microbiology and Infectious Diseases
|July 1, 2020
PubMed
Summary
This summary is machine-generated.

Digitalization and machine learning will transform clinical microbiology labs. Understanding data handling is key to leveraging artificial intelligence and big data for improved diagnostics and workflows.

Keywords:
AnalyticsArtificial intelligenceDiagnosticsDigitalizationImage analysisInteroperabilityMicrobiologyPost-analyticsPre-analyticsQuality

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Clinical Diagnostics

Background:

  • Digitalization and AI are reshaping future microbiology laboratory operations.
  • Significant opportunities and challenges exist in digitalizing microbiological workflows.
  • Effective use of big data, machine learning (ML), and artificial intelligence (AI) in clinical microbiology necessitates a deep understanding of data handling.

Purpose of the Study:

  • To review key concepts in digital microbiology for microbiologists, clinicians, and data scientists.
  • To provide practical examples of digital applications throughout the diagnostic process.
  • To offer insights into the opportunities and challenges of digitalizing diagnostic processes.

Main Methods:

  • Literature review using PubMed.
  • Search terms included digitalization, machine learning, artificial intelligence, and microbiology.
  • Analysis of peer-reviewed literature.

Main Results:

  • Digitalization presents opportunities and challenges in microbiological diagnostics.
  • Key aspects of data structure, interoperability, and legal considerations are crucial.
  • The review outlines pathways for implementing digital technologies in modern labs.

Conclusions:

  • Digitalization and ML will profoundly impact daily laboratory routines.
  • Identifying steps in the analytical process where digital technologies offer benefits is essential.
  • Adapting staff education is critical to prepare for advances in digital microbiology.