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

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.