Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Artificial Intelligence in MALDI-TOF MS: Microbial Identification, Strain Typing, and Antimicrobial Resistance Detection

Hsin-Yao Wang
* ORCID logo ,
Chia-Ru Chung
ORCID logo ,
Wan-Ying Lin
ORCID logo ,
Sriram Kalpana
,
Jorng-Tzong Horng
,
Tzong-Yi Lee
,
Jang-Jih Lu
Version 1 : Received: 14 December 2023 / Approved: 15 December 2023 / Online: 15 December 2023 (08:57:01 CET)

How to cite: Wang, H.; Chung, C.; Lin, W.; Kalpana, S.; Horng, J.; Lee, T.; Lu, J. Artificial Intelligence in MALDI-TOF MS: Microbial Identification, Strain Typing, and Antimicrobial Resistance Detection. Preprints 2023, 2023121149. https://doi.org/10.20944/preprints202312.1149.v1 Wang, H.; Chung, C.; Lin, W.; Kalpana, S.; Horng, J.; Lee, T.; Lu, J. Artificial Intelligence in MALDI-TOF MS: Microbial Identification, Strain Typing, and Antimicrobial Resistance Detection. Preprints 2023, 2023121149. https://doi.org/10.20944/preprints202312.1149.v1

Abstract

Globally, the use of whole-cell matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) in clinical microbiology laboratories has increased considerably. With the rapid growth of artificial intelligence technology, there is an increasing number of areas devoted to solving problems by implementing machine learning-based methods. Applying the use of artificial intelligence to the analysis of the massive amount of information obtained in the mass spectrum derived from MALDI-TOF MS should be a breakthrough in clinical microbiology. Specifically, it would facilitate a more rapid and accurate strain typing or antibiotic susceptibility test (AST) based on whole-cell MALDI-TOF MS spectra, and hence, it is a promising solution. Even though the reproducibility of peaks could result in some critical problems, computational methods have been developed to address this issue. Therefore, clinical microbiologists are able to perform strain typing or an AST as smoothly and easily as identifying species by using MALDI-TOF MS and artificial intelligence.

Keywords

artificial intelligence; antimicrobial susceptibility testing; antimicrobial typing; MALDI-TOF

Subject

Medicine and Pharmacology, Pathology and Pathobiology

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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