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Raman Spectroscopy: Overview01:20

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Discrimination of Rheumatoid and Psoriatic Arthritis Based on Raman and NIR Spectra Using Machine-Learning

Przemysław Cuprych1, Izabela Kokot2, Roman Szostak1

  • 1Department of Chemistry, University of Wroclaw, F. Joliot-Curie, 50-383 Wroclaw, Poland.

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Summary

Vibrational spectroscopy effectively differentiates rheumatoid arthritis (RA) and psoriatic arthritis (PsA) using blood serum spectra. This method aids in diagnosing these autoimmune diseases, which share similar symptoms and lack specific markers.

Keywords:
NIR spectroscopyRaman spectroscopydiscriminant analysismachine learningpsoriatic arthritisrheumatoid arthritis

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Area of Science:

  • Biomedical Spectroscopy
  • Chemometrics
  • Autoimmune Disease Diagnostics

Background:

  • Rheumatoid arthritis (RA) and psoriatic arthritis (PsA) are chronic autoimmune diseases with overlapping symptoms.
  • Lack of specific biomarkers complicates accurate diagnosis, leading to potential misclassification.
  • Spectroscopic analysis of body fluid composition offers a potential method for disease differentiation.

Purpose of the Study:

  • To evaluate the efficacy of Raman and near-infrared (NIR) spectroscopy combined with chemometric methods for distinguishing RA and PsA.
  • To compare the performance of partial least squares discriminant analysis (PLS-DA) and counter-propagation artificial neural network (CP-ANN) models.
  • To assess the diagnostic accuracy of models utilizing spectral data and biochemical parameters from blood serum.

Main Methods:

  • Analysis of freeze-dried blood sera from RA (n=30), PsA (n=24), and healthy controls (HC, n=15) using Raman and NIR spectroscopy.
  • Application of interval partial least squares (iPLS) for spectral feature selection.
  • Development and validation of PLS-DA and CP-ANN models, including hybrid models incorporating biochemical data.

Main Results:

  • PLS-DA and CP-ANN models based on selected spectral features achieved an overall accuracy (OA) of 81.3-93.8% for differentiating RA, PsA, and HC.
  • Hybrid models combining spectral variables and biochemical parameters demonstrated high OA values ranging from 87.5% to 93.8%.
  • The study successfully discriminated between RA and PsA using spectral data from dried blood serum.

Conclusions:

  • Vibrational spectroscopy (Raman and NIR) coupled with chemometric modeling provides a reliable approach for differentiating RA and PsA.
  • The developed models show significant potential for improving the diagnosis of these challenging autoimmune conditions.
  • Spectroscopic analysis of blood serum offers a non-invasive and effective strategy for distinguishing between RA and PsA.