Neural Network Enables High Accuracy for Hepatitis B Surface Antigen Detection with a Plasmonic Platform.
Journal:
Nano letters
PMID:
38975746
Abstract
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.