Raman spectroscopy with an improved support vector machine for discrimination of thyroid and parathyroid tissues.

Journal: Journal of biophotonics
Published Date: Jun 18, 2024

Abstract

The objective of this study was to discriminate thyroid and parathyroid tissues using Raman spectroscopy combined with an improved support vector machine (SVM) algorithm. In thyroid surgery, there is a risk of inadvertently removing the parathyroid glands. At present, there is a lack of research on using Raman spectroscopy to discriminate parathyroid and thyroid tissues. In this article, samples were obtained from 43 individuals with thyroid and parathyroid tissues for Raman spectroscopy analysis. This study employed partial least squares (PLS) to reduce dimensions of data, and three optimization algorithms are used to improve the classification accuracy of SVM algorithm model in spectral analysis. The results show that PLS-GA-SVM algorithm has higher diagnostic accuracy and better reliability. The sensitivity of this algorithm is 94.67% and the accuracy is 94.44%. It can be concluded that Raman spectroscopy combined with the PLS-GA-SVM diagnostic algorithm has significant potential for discriminating thyroid and parathyroid tissues.

Authors

  • Jie Hu
    Corteva Agriscience, Farming Solutions and Digital, Indianapolis, IN, United States.
  • Jinyu Xing
  • Pengfei Shao
    Department of Urology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
  • Xiaopeng Ma
    First Affiliated Hospital, University of Science and Technology of China, Hefei, China.
  • Peikun Li
    General Surgery Department, Second Affiliated Hospital of Anhui Medical University, Hefei, China.
  • Peng Liu
    Department of Clinical Pharmacy, Dazhou Central Hospital, Dazhou 635000, China.
  • Ru Zhang
    School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, China.
  • Wei Chen
    Department of Urology, Zigong Fourth People's Hospital, Sichuan, China.
  • Wang Lei
    General Surgery Department, Second Affiliated Hospital of Anhui Medical University, Hefei, China.
  • Ronald X Xu
    Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui, China.

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