To combat antibiotic resistance, it is extremely important to select the right antibiotic by performing rapid diagnosis of pathogens. Traditional techniques require complicated sample preparation and time-consuming processes that are...
Over the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door—antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing antibiotic resistance development, bacterial identification techniques include some challenging processes. To address this challenge, we proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy (SERS). Stacked autoencoder (SAE)-based DNN was used for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria using a label-free SERS technique. The performance of the DNN was compared with traditional classifiers. Since the SERS technique provides high signal-to-noise ratio (SNR) data, some subtle differences were found between MRSA and MSSA in relative band intensities. SAE-based DNN can learn features from raw data and classify them with an accuracy of 97.66%. Moreover, the model discriminates bacteria with an area under curve (AUC) of 0.99. Compared to traditional classifiers, SAE-based DNN was found superior in accuracy and AUC values. The obtained results are also supported by statistical analysis. These results demonstrate that deep learning has great potential to characterize and detect antibiotic-resistant bacteria by using SERS spectral data.
Bacterial pathogens especially antibiotic-resistant ones
are a
public health concern worldwide. To oppose the morbidity and mortality
associated with them, it is critical to select an appropriate antibiotic
by performing a rapid bacterial diagnosis. Using a combination of
Raman spectroscopy and deep learning algorithms to identify bacteria
is a rapid and reliable method. Nevertheless, due to the loss of information
during training a model, some deep learning algorithms suffer from
low accuracy. Herein, we modify the U-Net architecture to fit our
purpose of classifying the one-dimensional Raman spectra. The proposed
U-Net model provides highly accurate identification of the 30 isolates
of bacteria and yeast, empiric treatment groups, and antimicrobial
resistance, thanks to its capability to concatenate and copy important
features from the encoder layers to the decoder layers, thereby decreasing
the data loss. The accuracies of the model for the 30-isolate level,
empiric treatment level, and antimicrobial resistance level tasks
are 86.3, 97.84, and 95%, respectively. The proposed deep learning
model has a high potential for not only bacterial identification but
also for other diagnostic purposes in the biomedical field.
COVID-19 has caused millions of cases and deaths all over the world since late 2019. Rapid detection of the virus is crucial to control its spread through a population. COVID-19...
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