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

• URL: http://arxiv.org/abs/2206.13933v1
• Date: Mon, 27 Jun 2022 14:27:05 GMT
• Title: Accurate and fast identification of minimally prepared bacteria phenotypes using Raman spectroscopy assisted by machine learning
• Authors: Benjamin Lundquist Thomsen, Jesper B. Christensen, Olga Rodenko, Iskander Usenov, Rasmus Birkholm Gr{\o}nnemose, Thomas Emil Andersen, and Mikael Lassen
• Abstract summary: We develop a machine learning technique to identify methicillin-resistant (MR) bacteria from methicillin-susceptible (MS) bacteria. We attain more than 96$%$ classification accuracy on a dataset consisting of 15 different classes and 95.6$%$ classification accuracy for six MR-MS bacteria species.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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 accuracy on a dataset consisting of 15 different classes and 95.6$\%$ classification accuracy for six MR-MS bacteria species. More importantly, our results are obtained using only fast and easy-to-produce training and test data

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