aceEnhanced classification and identification of bacterial and viral microorganisms by integration of MALDI-TOF mass spectrometry with artificial intelligence
The accurate and rapid identification of bacterial pathogens is essential in clinical setups and medical biodefense. Matrix-Assisted Laser Desorption Ionization Time-Of-Flight (MALDI-TOF) mass spectrometry has emerged as a powerful tool for fast and reliable microbial identification. This study assesses the performance of eight Machine Learning (ML) and two Deep Learning (DL) models trained using 5-fold cross validation in classifying microorganisms in a series of experiments based on MALDI-TOF mass spectra (n =…
Machine learning and deep learning models trained on MALDI-TOF spectra improved rapid classification of bacteria versus viruses, Gram-positive versus Gram-negative, and individual species, with Extra Trees showing best generalization to an external highly pathogenic bacteria dataset.
Findings are based on a small internal dataset of 255 spectra from only seven bacterial and five viral agents, limiting breadth of species evaluated before external validation.
Evidence
- Peer-reviewedScientific Reports2026-08-24
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Truvace Impact Record TRV-2026-0880, v1: “Enhanced classification and identification of bacterial and viral microorganisms by integration of MALDI-TOF mass spectrometry with artificial intelligence.” Truvace, 2026-08-25. /record/TRV-2026-0880 (accessed at citation time). sha256 7575a2a103edaa63…
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