Yao Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Yao Li has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Chengguo Zuo
State Key Laboratory of Ophthalmology, Guangzhou 510060, China
1 shared publication - Huishan Lin
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology Visual Science, Guangzhou 510060, China
1 shared publication - Mingkai Lin
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
1 shared publication - Qiming He
Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, China
1 shared publication - Tao Xu
Tsinghua University
1 shared publication
Latest Publications
- Open Access
- 25 May 2023
- Published by MDPI AG in Applied Sciences
- Vol. 13 (11) , 6476
Label-Free Detection and Classification of Glaucoma Based on Drop-Coating Deposition Raman Spectroscopy
Primary open-angle glaucoma (POAG) and primary angle-closure glaucoma (PACG) are prevailing eye diseases that can lead to blindness. In order to provide a non-invasive diagnostic method for glaucoma, we investigated the feasibility of using drop-coating deposition Raman spectroscopy (DCDRS) to discriminate glaucoma patients from healthy individuals based on tear samples. Tears from 27, 19 and 27 POAG patients, PACG patients and normal individuals, respectively, were collected for Raman measurement. For high-dimension data analysis, principal component analysis–linear discriminant analysis (PCA-LDA) was used to discriminate the features of the Raman spectra, followed by a support vector machine (SVM) used to classify samples into three categories, which is called a PCA-LDA-based SVM. The differences in the characteristic peaks of Raman spectra between glaucoma patients and normal people were related to the different contents of various proteins and lipids. For the PCA-LDA-based SVM, the total accuracy reached 93.2%. With the evaluation of 30% test dataset validation, the classification accuracy of the model was 90.9%. The results of this work reveal that tears can be used for Raman detection and discrimination by combining the process with the PCA-LDA-based SVM, supporting DCDRS being a potential method for the diagnosis of glaucoma in the future.
- 25 May 2023
- Published by MDPI AG in Applied Sciences
- Vol. 13 (11) , 6476
Open AccessLabel-Free Detection and Classification of Glaucoma Based on Drop-Coating Deposition Raman Spectroscopy
Primary open-angle glaucoma (POAG) and primary angle-closure glaucoma (PACG) are prevailing eye diseases that can lead to blindness. In order to provide a non-invasive diagnostic method for glaucoma, we investigated the feasibility of using drop-coating deposition Raman spectroscopy (DCDRS) to discriminate glaucoma patients from healthy individuals based on tear samples. Tears from 27, 19 and 27 POAG patients, PACG patients and normal individuals, respectively, were collected for Raman measurement. For high-dimension data analysis, principal component analysis–linear discriminant analysis (PCA-LDA) was used to discriminate the features of the Raman spectra, followed by a support vector machine (SVM) used to classify samples into three categories, which is called a PCA-LDA-based SVM. The differences in the characteristic peaks of Raman spectra between glaucoma patients and normal people were related to the different contents of various proteins and lipids. For the PCA-LDA-based SVM, the total accuracy reached 93.2%. With the evaluation of 30% test dataset validation, the classification accuracy of the model was 90.9%. The results of this work reveal that tears can be used for Raman detection and discrimination by combining the process with the PCA-LDA-based SVM, supporting DCDRS being a potential method for the diagnosis of glaucoma in the future.