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Updated: May 31, 2026

Identification of Rare Bacterial Pathogens by 16S rRNA Gene Sequencing and MALDI-TOF MS

Identification of Rare Bacterial Pathogens by 16S rRNA Gene Sequencing and MALDI-TOF MS

Published on: July 11, 2016

Expert systems in clinical microbiology.

Trevor Winstanley¹, Patrice Courvalin

¹Department of Microbiology, Royal Hallamshire Hospital, Sheffield, United Kingdom. tgwinstanley@hotmail.com

Clinical Microbiology Reviews

|July 8, 2011

Summary

Expert systems in clinical microbiology aid in interpreting results, especially with automated systems. This review critically evaluates their performance in detecting antimicrobial resistance mechanisms.

Area of Science:

Medical Informatics
Clinical Microbiology
Artificial Intelligence in Medicine

Background:

Expert systems offer advanced data interpretation in medicine.
Clinical microbiology increasingly utilizes automated systems for diagnostics.
Antimicrobial resistance detection requires sophisticated analytical tools.

Purpose of the Study:

To review expert systems in general and their application in clinical microbiology.
To evaluate both noncommercial and commercial expert systems, including major automated platforms.
To critically assess the performance of these systems in detecting resistance mechanisms.

Main Methods:

Review of rule-based systems, pattern-based systems, data mining, and neural networks.
Description of noncommercial systems and the role of EUCAST (European Committee on Antimicrobial Susceptibility Testing).

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Identification of Rare Bacterial Pathogens by 16S rRNA Gene Sequencing and MALDI-TOF MS

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Published on: July 11, 2016

Direct Microbial Identification using An Automated Microbial Identification System to Facilitate the EUCAST RAST Method Without Mass Spectrometry

Direct Microbial Identification using An Automated Microbial Identification System to Facilitate the EUCAST RAST Method Without Mass Spectrometry

Published on: May 24, 2024

Critical analysis of published performance evaluations for commercial systems (Vitek 2, BD Phoenix, MicroScan).

Main Results:

Expert systems, including those on automated platforms, are crucial for interpreting complex microbiological data.
Commercial systems like Vitek 2, BD Phoenix, and MicroScan show varied performance in detecting specific resistance mechanisms.
The review highlights the necessity of expert rules in the context of updated EUCAST breakpoints.

Conclusions:

Expert systems are integral to modern clinical microbiology, enhancing diagnostic accuracy.
Automated systems with on-board expert systems significantly impact the detection of antimicrobial resistance.
Critical evaluation of system performance is essential for optimizing antimicrobial stewardship and patient care.