Digital microbiology

Abstract

Background: Digitalization and artificial intelligence have an important impact on the way microbiology laboratories will work in the near future. Opportunities and challenges lie ahead to digitalize the microbiological workflows. Making efficient use of big data, machine learning, and artificial intelligence in clinical microbiology requires a profound understanding of data handling aspects.

Objective: This review article summarizes the most important concepts of digital microbiology. The article gives microbiologists, clinicians and data scientists a viewpoint and practical examples along the diagnostic process.

Sources: We used peer-reviewed literature identified by a PubMed search for digitalization, machine learning, artificial intelligence and microbiology.

Content: We describe the opportunities and challenges of digitalization in microbiological diagnostic processes with various examples. We also provide in this context key aspects of data structure and interoperability, as well as legal aspects. Finally, we outline the way for applications in a modern microbiology laboratory.

Implications: We predict that digitalization and the usage of machine learning will have a profound impact on the daily routine of laboratory staff. Along the analytical process, the most important steps should be identified, where digital technologies can be applied and provide a benefit. The education of all staff involved should be adapted to prepare for the advances in digital microbiology.

Keywords: Analytics; Artificial intelligence; Diagnostics; Digitalization; Image analysis; Interoperability; Microbiology; Post-analytics; Pre-analytics; Quality.

Fig. 1

Concept of data handling within and across institutions. Local data warehouses with local cluster computers transfer interconnected and interoperable data for diagnostics, research and development to larger clusters allowing the enrichment of datasets. Clinical Data Warehouse (CDWH), Clinical Information System (CIS), Laboratory Information System (LIS), Radiology Information System (RIS).