Gold-viral particle identification by deep learning in wide-field photon scattering parametric images

Abstract

The ability to identify virus particles is important for research and clinical applications. Because of the optical diffraction limit, conventional optical microscopes are generally not suitable for virus particle detection, and higher resolution instruments such as transmission electron microscopy (TEM) and scanning electron microscopy (SEM) are required. In this paper, we propose a new method for identifying virus particles based on polarization parametric indirect microscopic imaging (PIMI) and deep learning techniques. By introducing an abrupt change of refractivity at the virus particle using antibody-conjugated gold nanoparticles (AuNPs), the strength of the photon scattering signal can be magnified. After acquiring the PIMI images, a deep learning method was applied to identify discriminating features and classify the virus particles, using electron microscopy (EM) images as the ground truth. Experimental results confirm that gold-virus particles can be identified in PIMI images with a high level of confidence.

Nanomaterials for virus sensing and tracking

“…Nanomaterials like AuNPs show high stability since they do not biodegrade or photobleach. [405][406][407][408] In addition to their extraordinary plasmonic properties, metallic nanoparticles strongly scatter incident light. 409 These signals can be recorded by microscopic techniques that exclude unscattered light from the recorded image.…”