等离子体子
抗原
人工神经网络
病毒学
表面等离子体子
材料科学
纳米技术
计算机科学
光电子学
免疫学
医学
人工智能
作者
NULL AUTHOR_ID,NULL AUTHOR_ID,Hongqin Xu,Li Wang,Junqi Niu,Junhu Zhang,NULL AUTHOR_ID
出处
期刊:Nano Letters
[American Chemical Society]
日期:2024-07-08
卷期号:24 (28): 8784-8792
被引量:1
标识
DOI:10.1021/acs.nanolett.4c02860
摘要
The detection of hepatitis B surface antigen (HBsAg) is critical in diagnosing hepatitis B virus (HBV) infection. However, existing clinical detection technologies inevitably cause certain inaccuracies, leading to delayed or unwarranted treatment. Here, we introduce a label-free plasmonic biosensing method based on the thickness-sensitive plasmonic coupling, combined with supervised deep learning (DL) using neural networks. The strategy of utilizing neural networks to process output data can reduce the limit of detection (LOD) of the sensor and significantly improve the accuracy (from 93.1%-97.4% to 99%-99.6%). Compared with widely used emerging clinical technologies, our platform achieves accurate decisions with higher sensitivity in a short assay time (∼30 min). The integration of DL models considerably simplifies the readout procedure, resulting in a substantial decrease in processing time. Our findings offer a promising avenue for developing high-precision molecular detection tools for point-of-care (POC) applications.
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