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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. In such biosensors, these particles are coated with a surface chemistry that makes them stick to the target analyte forming clusters in response. 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, UCLA researchers have developed a rapid and automated biosensing method based on holography coupled with deep learning – currently, one of the most promising and successfully used methods in artificial intelligence, AI. 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. Next, a trained deep neural network processes these holograms and rapidly reconstructs them into images of clusters similar to those that could be obtained with a standard scanning microscope,...
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