Abstract: A system for detecting the presence of and/or quantifying the amount or concentration of one or more analytes in a sample includes a flow assay cartridge having a multiplexed sensing membrane that has immunoreaction or biological reaction spots of varying conditions spatially arranged across the surface of the membrane defining an optimized spot map. A reader device is provided that uses a camera to image the multiplexed sensing membrane. Image processing software obtains normalized pixel intensity values of the plurality of immunoreaction or biological reaction spots and which are used as inputs to one or more trained neural networks configured to generate one or more outputs that: (i) quantify the amount or concentration of the one or more analytes in the sample; and/or (ii) indicate the presence of the one or more analytes in the sample; and/or (ii) determines a diagnostic decision or classification of the sample.

Abstract: A deep learning-based spectral analysis device and method are disclosed that employs a spectral encoder chip containing a plurality of nanohole array tiles, each with a unique geometry and, thus, a unique optical transmission spectrum. Illumination impinges upon the encoder chip and a CMOS image sensor captures the transmitted light, without any lenses, gratings, or other optical components. A spectral reconstruction neural network uses the transmitted intensities from the image to faithfully reconstruct the input spectrum. In one embodiment that used a spectral encoder chip with 252 nanohole array tiles, the network was trained on 50,352 spectra randomly generated by a supercontinuum laser and blindly tested on 14,648 unseen spectra. The system identified 96.86% of spectral peaks, with a peak localization error of 0.19 nm, peak height error of 7.60%, and peak bandwidth error of 0.18 nm.