Label-Free Detection and Classification of Glaucoma Based on Drop-Coating Deposition Raman Spectroscopy

Abstract

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.

Applications of Raman spectroscopy in ocular biofluid detection

“…Wu et al classified keratitis and conjunctivitis from healthy subjects using RS and deep learning models such as CNN and RNN with accuracies greater than 90%, indicating the great potential of RS of tears combined with depth learning algorithms in clinical screening of keratitis and conjunctivitis ( Wu et al, 2022 ). Li et al distinguished open-angle glaucoma (POAG), primary angle-closure glaucoma (PACG) patients, and a normal group using DCDRS of tears and pointed out the Raman spectroscopic features to reflect chemical information related to amino acids, proteins, and lipids in tears ( Li et al, 2023b ). The accuracy of classification of the samples using an SVM model based on PCA-LDA dimensionality reduction is over 90%, indicating the feasibility of using tear samples for glaucoma diagnosis.…”

Central and Peripheral Fatigue Evaluation during Physical Exercise in Athletic Horses by Means of Raman Spectroscopy

“…Raman spectroscopy, a technique for the analysis of biological fluids, which has been proven effective for the conformational analysis of proteins [ 10 , 11 , 12 ], was used in our study. Raman spectroscopy is widely used in the study of human biofluids (e.g., tears [ 13 ], urine [ 14 ], and sweat [ 15 ]).…”