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The worldwide increase of antimicrobial resistance (AMR) is a serious threatto human health. To avert the spread of AMR, fast reliable diagnostics toolsthat facilitate optimal antibiotic stewardship are an unmet need. In thisregard, Raman spectroscopy promises rapid label- and culture-freeidentification and antimicrobial susceptibility testing (AST) in a single step.However, even though many Raman-based bacteria-identification and AST studieshave demonstrated impressive results, some shortcomings must be addressed. Tobridge the gap between proof-of-concept studies and clinical application, wehave developed machine learning techniques in combination with a noveldata-augmentation algorithm, for fast identification of minimally preparedbacteria phenotypes and the distinctions of methicillin-resistant (MR) frommethicillin-susceptible (MS) bacteria. For this we have implemented a spectraltransformer model for hyper-spectral Raman images of bacteria. We show that ourmodel outperforms the standard convolutional neural network models on amultitude of classification problems, both in terms of accuracy and in terms oftraining time. We attain more than 96$\%$ classification accuracy on a datasetconsisting of 15 different classes and 95.6$\%$ classification accuracy for sixMR-MS bacteria species. More importantly, our results are obtained using onlyfast and easy-to-produce training and test data

Accurate and fast identification of minimally prepared bacteria phenotypes using Raman spectroscopy assisted by machine learning