|
Raman spectroscopy is a widely used analytical technique that provides extensive information about the chemical composition and molecular structure of samples. It is based on the Raman scattering phenomenon, where when a sample is irradiated with excitation light, photon scattering occurs, causing a slight shift in frequency that reflects the vibrational and rotational states of molecules in the sample. By analyzing these frequency shifts, one can understand the types of chemical bonds, molecular configurations, and other relevant information within the sample. Osteoarthritis, the most prevalent joint disorder, is typified by the degradation of articular cartilage and the engagement of all tissues within the joint. eventually leading to cartilage degeneration, fibrosis, rupture, defects, and damage to the entire joint surface. Therefore, timely and accurate diagnosis and treatment of patients are crucial. In this study, we aimed to achieve an objective, rapid, and accurate diagnosis of osteoarthritis using serum Raman spectrum combined with deep learning methods. In this experiment, serum samples were collected from 116 osteoarthritis patients and 116 healthy control subjects, and Raman spectroscopy data were obtained. The collected spectral data were preprocessed by the adaptive iteratively reweighted penalized least squares (airPLS) and Savitzky-Golay (SG) filtering algorithms. CNN and TCN classification models were selected to classify and identify osteoarthritis patients and healthy controls. The results showed that TCN had the excellent identification performance, with an average accuracy, sensitivity, and specificity of 97.03%, 100%, and 82.86%, respectively, over five experiments. The area under the ROC curve (AUC) was also the highest at 0.97. These experimental results indicate that deep learning methods based on serum Raman spectroscopy have great potential in the rapid diagnosis of osteoarthritis and can provide reference for the auxiliary diagnosis of other diseases.
|
