Uric acid (UA) is an important biomarker for many diseases. A sensitive point-of-care (POC) testing platform is designed for the digital quantification of salivary UA based on a colorimetric reaction on an easy-to-build smartphone-assisted microfluidic paper-based analytical device (SμPAD). UA levels are quantified according to the color intensity of Prussian blue on the SμPAD with the aid of a MATLAB code or a smartphone APP. A color correction method is specifically applied to exclude the light effect. Together with the engineering design of SμPADs, the background calibration function with the APP increases the UA sensitivity by 100-fold to reach 0.1 ppm with a linear range of 0.1−200 ppm. The assay time is less than 10 min. SμPADs demonstrate a correlation of 0.97 with a commercial UA kit for the detection of salivary UA in clinical samples. SμPADs provide a sensitive, fast, affordable, and reliable tool for the noninvasive POC quantification of salivary UA for early diagnosis of abnormal UA level-associated health conditions.

The use of smartphone‐based analysis systems has been increasing over the past few decades. Among the important reasons for its popularity are its ubiquity, increasing computing power, relatively low cost, and capability to acquire and process data simultaneously in a point‐of‐need fashion. Furthermore, smartphones are equipped with various sensors, especially a complementary metal–oxide–semiconductor (CMOS) sensor. The high sensitivity of the CMOS sensor allows smartphones to be used as a colorimeter, fluorimeter, and spectrometer, constituting the essential part of point‐of‐care testing contributing to E‐health and beyond. However, despite its myriads of merits, smartphone‐based diagnostic devices still face many challenges, including high susceptibility to illumination conditions, difficulty in adapter uniformization, low interphone repeatability, and et al. These problems may hinder smartphone‐enabled diagnosis from passing the FDA regulations of medical devices. This review discusses the design and application of current smartphone‐based diagnostic devices and highlights challenges associated with existent methods and perspectives on how to deal with those challenges from engineering aspects on constant color signal acquisition, including smartphone adapter design, color space transformation, machine learning classification, and color correction.