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Discrimination of Dengue Diseases in Children Using Surface-Enhanced Raman Spectroscopy Coupled with Machine Learning

Uraiwan Waiwijit1, Pitak Eiamchai1, Saksorn Limwichean1

  • 1Spectroscopic and Sensing Devices Research Group, National Electronics and Computer Technology (NECTEC), National Science and Technology Development Agency (NSTDA), Pathum Thani 12120, Thailand.

Analytical Chemistry
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Summary

This study uses surface-enhanced Raman spectroscopy (SERS) and machine learning for rapid dengue virus (DENV) diagnostics. The novel method accurately identifies dengue infection and predicts disease severity, outperforming current techniques.

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Area of Science:

  • Biomedical Diagnostics
  • Spectroscopy
  • Machine Learning

Background:

  • Dengue virus (DENV) infection requires rapid and accurate diagnostics.
  • Existing diagnostic methods face limitations in speed and accuracy, especially in resource-limited settings.
  • Predicting disease severity is crucial for effective patient management.

Purpose of the Study:

  • To introduce a novel diagnostic approach for dengue using surface-enhanced Raman spectroscopy (SERS) coupled with machine learning.
  • To assess the capability of SERS and machine learning in identifying DENV infection and differentiating disease severity.
  • To evaluate the performance of SERS-based diagnostics against existing methods.

Main Methods:

  • Analysis of plasma samples from 60 pediatric patients using a commercialized SERS substrate.
  • Application of machine learning algorithms, including linear discriminant analysis (LDA) and logistic regression, for spectral data classification.
  • Distinguishing between other febrile illnesses (OFI), dengue fever (DF), and dengue hemorrhagic fever (DHF) cases.

Main Results:

  • High accuracy in distinguishing dengue from OFI (AUC of 0.99 for both LDA and logistic regression).
  • Effective discrimination between DF, DHF, and OFI, with LDA achieving AUCs of 0.81, 0.90, and 0.99, and logistic regression achieving 0.82, 0.88, and 0.99, respectively.
  • Notable performance in differentiating DF from DHF (AUC of 0.84 for LDA and 0.79 for logistic regression).

Conclusions:

  • The SERS-based approach offers a significant advancement in dengue diagnostics, providing speed and accuracy.
  • This innovative technique has the potential to improve early detection and classification of dengue severity.
  • The method is particularly promising for resource-limited settings, aiding patient outcomes and therapeutic strategies.