'Fingerprint' machine learning technique identifies different bacteria
in seconds

Date:
March 4, 2022
Source:
The Korea Advanced Institute of Science and Technology (KAIST)
Summary:
Bacterial identification can take hours and often longer --
precious time when diagnosing infections and selecting appropriate
treatments. There may be a quicker, more accurate process. By
teaching a deep learning algorithm to identify the 'fingerprint'
spectra of the molecular components of various bacteria, the
researchers could classify various bacteria in different media
with accuracies up to 98%.



FULL STORY ========================================================================== Bacterial identification can take hours and often longer, precious time
when diagnosing infections and selecting appropriate treatments. There may
be a quicker, more accurate process according to researchers at KAIST. By teaching a deep learning algorithm to identify the "fingerprint" spectra
of the molecular components of various bacteria, the researchers could
classify various bacteria in different media with accuracies of up to 98%.


========================================================================== Their results were made available online on Jan. 18 in Biosensors and Bioelectronics, ahead of publication in the journal's April issue.

Bacteria-induced illnesses, those caused by direct bacterial infection
or by exposure to bacterial toxins, can induce painful symptoms and even
lead to death, so the rapid detection of bacteria is crucial to prevent
the intake of contaminated foods and to diagnose infections from clinical samples, such as urine. "By using surface-enhanced Raman spectroscopy
(SERS) analysis boosted with a newly proposed deep learning model, we demonstrated a markedly simple, fast, and effective route to classify
the signals of two common bacteria and their resident media without
any separation procedures," said Professor Sungho Jo from the School
of Computing.

Raman spectroscopy sends light through a sample to see how it
scatters. The results reveal structural information about the sample
-- the spectral fingerprint -- allowing researchers to identify its
molecules. The surface- enhanced version places sample cells on noble
metal nanostructures that help amplify the sample's signals.

However, it is challenging to obtain consistent and clear spectra of
bacteria due to numerous overlapping peak sources, such as proteins in
cell walls.

"Moreover, strong signals of surrounding media are also enhanced to
overwhelm target signals, requiring time-consuming and tedious bacterial separation steps," said Professor Yeon Sik Jung from the Department of Materials Science and Engineering.

To parse through the noisy signals, the researchers implemented
an artificial intelligence method called deep learning that can
hierarchically extract certain features of the spectral information
to classify data. They specifically designed their model, named the
dual-branch wide-kernel network (DualWKNet), to efficiently learn the correlation between spectral features.

Such an ability is critical for analyzing one-dimensional spectral data, according to Professor Jo.

"Despite having interfering signals or noise from the media, which
make the general shapes of different bacterial spectra and their
residing media signals look similar, high classification accuracies
of bacterial types and their media were achieved," Professor Jo said, explaining that DualWKNet allowed the team to identify key peaks in each
class that were almost indiscernible in individual spectra, enhancing
the classification accuracies. "Ultimately, with the use of DualWKNet
replacing the bacteria and media separation steps, our method dramatically reduces analysis time." The researchers plan to use their platform to
study more bacteria and media types, using the information to build a
training data library of various bacterial types in additional media to
reduce the collection and detection times for new samples.

"We developed a meaningful universal platform for rapid bacterial
detection with the collaboration between SERS and deep learning,"
Professor Jo said. "We hope to extend the use of our deep learning-based
SERS analysis platform to detect numerous types of bacteria in additional
media that are important for food or clinical analysis, such as blood."
The National R&D Program, through a National Research Foundation of Korea
grant funded by the Ministry of Science and ICT, supported this research.

========================================================================== Story Source: Materials provided by The_Korea_Advanced_Institute_of_Science_and_Technology_ (KAIST). Note:
Content may be edited for style and length.


========================================================================== Related Multimedia:
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Schematics_of_the_general_process_of_Raman_data_collection_and_analysis ========================================================================== Journal Reference:
1. Eojin Rho, Minjoon Kim, Seunghee H. Cho, Bongjae Choi, Hyungjoon
Park,
Hanhwi Jang, Yeon Sik Jung, Sungho Jo. Separation-free bacterial
identification in arbitrary media via deep neural network-based
SERS analysis. Biosensors and Bioelectronics, 2022; 202: 113991 DOI:
10.1016/ j.bios.2022.113991 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/03/220304101005.htm

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