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

TL;DR

This study introduces a machine learning approach with a novel data-augmentation method for rapid, accurate identification of bacteria phenotypes and antibiotic resistance using Raman spectroscopy, aiming to improve clinical diagnostics.

Contribution

It presents a spectral transformer model that outperforms standard CNNs in classifying bacteria and resistance, using minimal and quickly obtained data.

Findings

Achieved over 96% accuracy on 15 bacteria classes

Attained 95.6% accuracy in distinguishing MR from MS bacteria

Model trained efficiently with minimal data

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