Systems and methods for high-dimensional contextualization of signal data from capillary-driven assays using machine learning algorithms

A method for determining a quantity of a target substance in a test medium using an assay device having capillary-driven flow, the method comprising:

• (a) defining, within the assay device, a region of interest (ROI) associated with development of one or more measurable signals during operation of the assay device;
• (b) partitioning the ROI into a plurality of measurement spatial units, each unit representing a spatial subdivision of the ROI;
• (c) obtaining, from the plurality of measurement spatial units, a plurality of contextual measurements {Yi}, each contextual measurement Yi representing a measurable property within a corresponding measurement spatial unit and characterizing physical or chemical conditions occurring within the ROI;
• (d) identifying, from among the contextual measurements {Yi}, one or more signal measurements comprising contextual measurements that include analyte-dependent information arising during operation of the assay device;
• (e) processing the contextual measurements {Yi} and the signal measurements using a machine-learning model configured to identify patterns or relationships within the measurements and to generate one or more intermediate representations;
• (f) computing a signal value X for the ROI as a function of the contextual measurements {Yi}, the intermediate representations, and a set of assay-specific parameters C, such that X=f({Yi}, C);
• and
• (g) determining a quantity of the target substance in the test medium based at least in part on the computed signal value X.