Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, China.
Institute of Advanced Technology, University of Science and Technology of China, Hefei, China.
J Biophotonics. 2024 Aug;17(8):e202400084. doi: 10.1002/jbio.202400084. Epub 2024 Jun 18.
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.
本研究旨在利用拉曼光谱结合改进的支持向量机（SVM）算法对甲状腺和甲状旁腺组织进行鉴别。在甲状腺手术中，存在无意中切除甲状旁腺的风险。目前，利用拉曼光谱来鉴别甲状旁腺和甲状腺组织的研究还很少。本文从 43 名甲状腺和甲状旁腺组织患者中获得样本进行拉曼光谱分析。本研究采用偏最小二乘法（PLS）对数据进行降维处理，并采用三种优化算法提高 SVM 算法模型在光谱分析中的分类准确性。结果表明，PLS-GA-SVM 算法具有更高的诊断准确性和更好的可靠性。该算法的灵敏度为 94.67%，准确率为 94.44%。可以得出结论，拉曼光谱结合 PLS-GA-SVM 诊断算法在鉴别甲状腺和甲状旁腺组织方面具有显著的潜力。
