Rapid screening for autoimmune diseases using Fourier transform infrared spectroscopy and deep learning algorithms - Wu, Xue; Shuai, Wei; Chen, Chen; Chen, Xiaomei; Luo, Cainan; Chen, Yi; Shi, Yamei; Li, Zhengfang; Lv, Xiaoyi; Chen, Cheng; Meng, Xinyan; Lei, Xin; et al. (Frontiers in Immunology, 2023)
Ankylosing spondylitis (AS), rheumatoid arthritis (RA), and osteoarthritis (OA) are three rheumatological immune disorders that share many standard features, resulting in degeneration of the joint cartilage and limitation of daily activities, which in severe cases, can lead to lifelong disability or even death. Therefore, achieving early diagnosis and identifying predictive biomarkers is becoming increasingly critical for personalized therapeutic strategies. This paper uses Fourier Transform Infrared Spectroscopy (FTIR) to achieve non-invasive, fast, and accurate differentiation between AS, RA, OA, and healthy controls based on a deep learning model. This approach offers a non-invasive, quick, and precise method for differentiation. Additionally, we evaluated the ability of FTIR to identify spectral biometric fingerprint substances. The experiment collected 320 serum samples, and a multi-scale combined residual block MSResNet classification model was developed to analyze the data. The model utilizes convolutional modules of different scales to extract features of various scales. It also employs Residual Blocks to address the problem of network degradation, reducing noise interference during spectral measurement and improving the generalization ability of the network model. Comparative analysis of experimental results from AlexNet, ResNet, MSCNN, and the MSResNet model revealed that the MSResNet model exhibited the highest diagnostic performance, achieving an accuracy of 0.87. These results demonstrate the practicality of combining serum FTIR spectroscopy with deep learning algorithms to differentiate between AS, RA, OA, and healthy controls. This approach can serve as an effective supplementary diagnostic method.