Communications of the ACM
'Fingerprint' ML Technique Identifies Bacteria in Seconds
By teaching a deep learning algorithm to identify the “fingerprint” spectra of the molecular components of various bacteria, Korea Advanced Institute of Science and Technology researchers were able to classify various bacteria in different med
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Researchers at the Korea Advanced Institute of Science and Technology (KAIST) combined surface-enhanced Raman spectroscopy and a deep learning model to identify bacteria in seconds with up to 98% accuracy.
Their model, named DualWKNet (dual-branch wide-kernel network), was trained to identify the "fingerprint" spectra of the molecular components of multiple bacteria.
Said KAIST's Sungho Jo, "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."
Jo added, "Ultimately, with the use of DualWKNet replacing the bacteria and media separation steps, our method dramatically reduces analysis time."
From KAIST (South Korea)
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