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

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.

Graphical abstract

Keywords

Graves’ ophthalmology / laser-induced breakdown spectroscopy (LIBS) / linear discriminant analysis (LDA) / support vector machine (SVM) / k-nearest neighbor (kNN) / generalized regression neural network (GRNN)

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 61575073). The authors would also like to acknowledge valuable discussions with the master student Haohao Cui.

Disclosures

The authors declare that there are no conflicts of interest related to this article.

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