Application of serum SERS technology combined with deep learning algorithm in the rapid diagnosis of immune diseases and chronic kidney disease

Abstract

Surface-enhanced Raman spectroscopy (SERS), as a rapid, non-invasive and reliable spectroscopic detection technique, has promising applications in disease screening and diagnosis. In this paper, an annealed silver nanoparticles/porous silicon Bragg reflector (AgNPs/PSB) composite SERS substrate with high sensitivity and strong stability was prepared by immersion plating and heat treatment using porous silicon Bragg reflector (PSB) as the substrate. The substrate combines the five deep learning algorithms of the improved AlexNet, ResNet, SqueezeNet, temporal convolutional network (TCN) and multiscale fusion convolutional neural network (MCNN). We constructed rapid screening models for patients with primary Sjögren’s syndrome (pSS) and healthy controls (HC), diabetic nephropathy patients (DN) and healthy controls (HC), respectively. The results showed that the annealed AgNPs/PSB composite SERS substrates performed well in diagnosing. Among them, the MCNN model had the best classification effect in the two groups of experiments, with an accuracy rate of 94.7% and 92.0%, respectively. Previous studies have indicated that the AgNPs/PSB composite SERS substrate, combined with machine learning algorithms, has achieved promising classification results in disease diagnosis. This study shows that SERS technology based on annealed AgNPs/PSB composite substrate combined with deep learning algorithm has a greater developmental prospect and research value in the early identification and screening of immune diseases and chronic kidney disease, providing reference ideas for non-invasive and rapid clinical medical diagnosis of patients.

Advances in uremic toxin detection and monitoring in the management of chronic kidney disease progression to end-stage renal disease

“…90,93 Furthermore, labelfree SERS exhibits high sensitivity and selectivity when combined with a labeled antibody-antigen system. [94][95][96] As shown in Fig. 3(iii), taking Au NPs on a bowl-like ZrO 2 substrate as an example, target analytes can SERS-active reporters, and the hotspots in each nanobowl enhance the SERS signal.…”

Recent Trends in Surface-Enhanced Raman Spectroscopy-Based Biosensors: Label-Free Early Disease Diagnosis

“…Wang et al identified different stages of AD with a residual network–based CNN algorithm with 98.9% accuracy. Yang et al designed a multiscale fusion CNN comprising three 1D convolutional layers, one flattening layer, and two completely connected layers for the rapid diagnosis of immune and chronic kidney diseases. The multiscale fusion CNN model exhibited remarkable stability in discriminating between healthy controls and diabetic nephropathy patients with higher accuracy (92.0%), sensitivity (95.6%), precision (86.7%), and AUC (0.972) than those of AlexNet, ResNet, SqueezeNet, and temporal convolution network .…”