Neural Network Enables High Accuracy for Hepatitis B Surface Antigen Detection with a Plasmonic Platform.
Nano Lett
; 24(28): 8784-8792, 2024 Jul 17.
Article
en En
| MEDLINE
| ID: mdl-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 (â¼30 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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Técnicas Biosensibles
/
Redes Neurales de la Computación
/
Hepatitis B
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Antígenos de Superficie de la Hepatitis B
Límite:
Humans
Idioma:
En
Revista:
Nano Lett
Año:
2024
Tipo del documento:
Article
Pais de publicación:
Estados Unidos