From Droplet to Diagnosis: Spatio‐Temporal Pattern Recognition in Drying Biofluids

Drying droplets of complex biofluids reveal a rich interplay of evaporation‐driven flows, phase segregation, and self‐assembly, resulting in intricate patterns that encode significant spatio‐temporal information. Whereas prior studies have predominantly emphasized spatial analysis, this work advances a framework that incorporates both spatial and temporal dimensions, leveraging machine learning (ML) for accurate compositional classification of blood samples. Systematic variations in initial concentration manifest as quantifiable differences in drying behavior, captured through spatio‐temporal imaging. Statistical features extracted from these image sequences enable traditional MLs to achieve 99% classification accuracy, outperforming deep learning (DL) that achieves 96% accuracy when tested directly on new image data. Gradient‐weighted Class Activation Mapping (Grad‐CAM) indicates that DL focuses on highly localized textural regions, revealing that the dynamic evolution of drying encodes more information than static end‐point images suggest. Importantly, the proposed framework extends beyond blood diagnostics, demonstrating broad applicability to microbial suspensions, protein solutions, and liquid crystals. This work introduces an interpretable, low‐volume, and sustainable analytical method, establishing drying droplets as a powerful, high‐throughput readout for fluid behavior across scientific disciplines.