Abstract Dried biofluid droplets contain complex physicochemical information, yet their diagnostic potential remains largely limited due to the absence of an accessible, end‐to‐end analytical pipeline. This study presents a generalizable, high‐throughput pipeline that, for the first time, seamlessly integrates image acquisition in three steps, namely (i) automated droplet detection and cropping, (ii) quantitative texture analysis, and (iii) interpretable machine learning for disease classification. The label‐free workflow operates with minimal user intervention, adaptable across imaging modalities and various biofluids, and experimental environments. Robustness of this pipeline is demonstrated on biofluids from both mice and humans, identifying disease‐specific morphological signatures of influenza and diabetes directly from dried droplet patterns. As prior studies have not characterized dried droplet morphologies for these diseases, this work establishes a foundational benchmark for image‐based diagnostic screening. By transforming endpoint morphologies into reproducible biomarkers, the pipeline enables rapid disease detection from blood dried patterns and is readily extendable to other biofluids, including saliva, sweat, and urine. Its simplicity, scalability, and cross‐species applicability make it ideally suited for point‐of‐care diagnostics, particularly in resource‐limited settings. To ensure reproducibility and facilitate broad adoption, open‐access analytical tools with a graphical user interface for droplet region selection are provided, promoting translation to diagnostic, veterinary, and biotechnological applications.
Pal et al. (Fri,) studied this question.