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Accurate and fast identification of minimally prepared bacteria phenotypes using Raman spectroscopy assisted by machine learning by Thomsen, Benjamin Lundquist; Christensen, Jesper B.; Rodenko, Olga; Usenov, Iskander; Grønnemose, Rasmus Birkholm; Andersen, Thomas Emil; Lassen, Mikael is a scholarly article available to read on EtoBox.

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

The worldwide increase of antimicrobial resistance (AMR) is a serious threat to human health. To avert the spread of AMR, fast reliable diagnostics tools that facilitate optimal antibiotic stewardship are an unmet need. In this regard, Raman spectroscopy promises rapid label- and culture-free identification and antimicrobial susceptibility testing (AST) in a single step. However, even though many Raman-based bacteria-identification and AST studies have demonstrated impressive results, some shortcomings must be addressed. To bridge the gap between proof-of-concept studies and clinical application, we have developed machine learning techniques in combination with a novel data-augmentation algorithm, for fast identification of minimally prepared bacteria phenotypes and the distinctions of methicillin-resistant (MR) from methicillin-susceptible (MS) bacteria. For this we have implemented a spectral transformer model for hyper-spectral Raman images of bacteria. We show that our model outperforms the standard convolutional neural network models on a multitude of classification problems, both in terms of accuracy and in terms of training time. We attain more than 96$\%$ classification acc

Author

Thomsen, Benjamin Lundquist; Christensen, Jesper B.; Rodenko, Olga; Usenov, Iskander; Grønnemose, Rasmus Birkholm; Andersen, Thomas Emil; Lassen, Mikael

Published

2022

Language

EN