Author:
Yang Jie,Chen Xiaomei,Luo Cainan,Li Zhengfang,Chen Chen,Han Shibin,Lv Xiaoyi,Wu Lijun,Chen Cheng
Abstract
AbstractSurface-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.
Funder
the Youth Science Fund of Natural Science Foundation of Xinjiang Uygur Autonomous Region
the Distinguished Young Talents Project of Natural Science Foundation of Xinjiang Uygur Autonomous Region
the project of scientific and technological assistance to Xinjiang
The Key Research and Development Project of Xinjiang Uygur Autonomous Region
Publisher
Springer Science and Business Media LLC
Cited by
5 articles.
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