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Artificial intelligence tech developed at UCLA detects the presence of viruses

To overcome the shortcomings of the existing biosensing application solutions, UCLA researchers have developed a rapid and automated biosensing method applying one of the most promising and successfully used methods in artificial intelligence, AI.
Many biosensing applications rely on characterization of specific analytes such as proteins, viruses and bacteria, among many other targets, which can be accomplished by using micro- or nano-scale particles, according to a university article.

In such biosensors, these particles are coated with a surface chemistry that makes them stick to the target analyte forming clusters in response, the article states.

The higher the target analyte concentration is, the larger the number of clusters gets. Therefore, monitoring and characterizing these particle clusters can tell us if the target analyte is present in a sample and in what concentration.

Current methods to perform such an analysis are limited in that they are either capable of only a coarse readout or rely on expensive and bulky microscopes, which limit their applicability to address different biosensing needs, especially in resource limited environments.

To overcome the shortcomings of the existing solutions, researchers have developed a rapid and automated biosensing method based on holography coupled with deep learning.

In this system, all the particle clusters and individual micro-particles in a sample are first imaged in 3-D as holograms, all at the same time, and over a very large sample area of more than 20 mm2, more than ten-fold larger than the imaging area of a standard optical microscope.

Read more HERE.