Abstract
<jats:p>Lateral flow assays (LFAs) are ubiquitous point-of-care diagnostics but suffer from high error rates driven by the "matrix effect" (ME), which is physicochemical sample heterogeneity inducing severe signal variance. This lack of analytical precision creates massive diagnostic gray zones, particularly in competitive LFAs. Here, we introduce the Whole-Process Analysis (WPA) paradigm. Instead of static endpoint readouts, WPA continuously captures LFA spatiotemporal evolution by using a deep learning architecture. This decodes dynamic high-dimensional "ME footprints", such as capillary flow velocity and early grey value flux, to algorithmically compensate for matrix interference. We validated WPA for xylazine testing across 251 patient urine samples, benchmarking against gold-standard liquid chromatography-tandem mass spectrometry (LC-MS/MS). WPA drastically compressed the diagnostic gray zone (area under the curve [AUC] = 0.988), delivering 94.02% overall accuracy, 100.00% positive predictive value, and 92.99% negative predictive value. By computationally untangling true biological signals from ME-induced noise, WPA provides a transformative, device-adaptable pathway to upgrade LFA precision without altering assay chemistry.</jats:p>