Label-free bacteria identification for clinical applications

Abstract

We have developed a system for bacteria identification based on absorption spectroscopy in the mid-infrared spectral range. The data collected are analyzed with a deep learning algorithm. It is based on a neural-network model which takes one-dimensional signal vectors and outputs a probability score of identification of a bacterium type by extracting micro and macro scale features, using convolutions and nonlinear operations. The results are achieved in real time and do not require any offline postprocessing. The study was done on 12 of the most common bacteria usually seen in clinical microbiology laboratories. The system sensitivity is 0.94 ± 0.04, with a specificity of 0.95 ± 0.02. The system can be extended to additional bacterium types and variants with no change to its hardware or software, but only updating the model's parameters. The system's accuracy, size, ease of operation and low cost make it suitable for use in any type of clinical setting.