A micro-ChromaDot array with AI integration for the detection of multiple biomarkers in a small portable device

With the rapid growth of digital healthcare, diagnosis, prognosis, and monitoring of chronic and acute diseases at home are increasingly in demand. In light of the intricate biological processes underlying many diseases, a single biomarker is often insufficient for disease diagnosis, whereas multiplexed measurements of biomarkers remain a significant challenge at point-of-care (POC). Herein, we introduce a micro-ChromaDot array (McDa) designed to qualify a panel of biomarkers at POC in a one-biomarker-one-dot fashion. The McDa colorimetric signals were engraved from a 3D microneedle array with chem-spatial signal amplification cascade, which allowed seamless and precise data acquisition and analysis by smartphone. This home-use and cost-effective platform achieved similar specificity and superior sensitivity to conventional enzyme-linked immunoassay (ELISA), a clinic assay that typically requires a fully equipped laboratory, and trained personnel, and is time-consuming. Five biomarkers of clinical sera, collected from a cohort of systemic lupus erythematosus (SLE) patients (n = 42) and healthy controls (n = 34), were measured by McDa and modeled with deep learning. By leveraging the support vector machines (SVM) model combined with 5-fold cross-validation, we demonstrated that a selected panel of three biomarkers effectively discriminated SLE patients from healthy controls with a remarkable 97% specificity, representing an impressive 70% improvement over the clinical standard of the single biomarker antinuclear antibodies (ANA) test, while sustaining the same 93% sensitivity. This platform can easily adapt to assess other biomarkers with a mere drop of blood or any other body liquids and holds great promise to transform POC.