Identification of Graves' ophthalmology by laser-induced breakdown spectroscopy combined with machine learning method.

Journal: Frontiers of optoelectronics
Published Date: Apr 14, 2020

Abstract

Diagnosis of the Graves' ophthalmology remains a significant challenge. We identified between Graves' ophthalmology tissues and healthy controls by using laser-induced breakdown spectroscopy (LIBS) combined with machine learning method. In this work, the paraffin-embedded samples of the Graves' ophthalmology were prepared for LIBS spectra acquisition. The metallic elements (Na, K, Al, Ca), non-metallic element (O) and molecular bands ((C-N), (C-O)) were selected for diagnosing Graves' ophthalmology. The selected spectral lines were inputted into the supervised classification methods including linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbor (kNN), and generalized regression neural network (GRNN), respectively. The results showed that the predicted accuracy rates of LDA, SVM, kNN, GRNN were 76.33%, 96.28%, 96.56%, and 96.33%, respectively. The sensitivity of four models were 75.89%, 93.78%, 96.78%, and 96.67%, respectively. The specificity of four models were 76.78%, 98.78%, 96.33%, and 96.00%, respectively. This demonstrated that LIBS assisted with a nonlinear model can be used to identify Graves' ophthalmopathy with a higher rate of accuracy. The kNN had the best performance by comparing the three nonlinear models. Therefore, LIBS combined with machine learning method can be an effective way to discriminate Graves' ophthalmology.

Authors

  • Jingjing Li
    Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China.
  • Feng Chen
    Department of Integrated Care Management Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Guangqian Huang
    Department of Ophthalmology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
  • Siyu Zhang
    Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China.
  • Weiliang Wang
    Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China.
  • Yun Tang
    Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China.
  • Yanwu Chu
    Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China.
  • Jian Yao
    School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China.
  • Lianbo Guo
    Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China. lbguo@hust.edu.cn.
  • Fagang Jiang
    Department of Ophthalmology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China. fgjiang@hotmail.com.

Keywords

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