Smartphone-Embedded Artificial Intelligence-Based Regression for Colorimetric Quantification of Multiple Analytes with a Microfluidic Paper-Based Analytical Device in Synthetic Tears

Abstract

Artificial intelligence (AI) and smartphones have attracted significant interest in microfluidic paper-based colorimetric sensing due to their convenience and robustness. Recently, AI-based classification of colorimetric assays has been increasingly reported. However, quantitative evaluation remains a challenge, as classification aims to categorize the color change into discrete class labels rather than a quantity. Therefore, in this study, an AI-based regression model with enhanced accuracy is developed and integrated into a microfluidic paper-based analytical device for simultaneous colorimetric measurements of glucose, cholesterol, and pH. The model is also embedded into a smartphone via a custom-designed Android application named ChemiCheck to complete on-site colorimetric quantification without internet access in under 1 s. The results demonstrate that the integrated system is able to sensitively detect both glucose (limit of detection [LOD]: 131 $\mu M$) and cholesterol (LOD: 217 $\mu M$), concluding the entire analysis within minutes while maintaining a maximum root mean square error of 0.386. Overall, the integrated platform holds great promise for point-of-care testing and offers numerous advantages, including easy-to-use operation, rapid response, low-cost, high selectivity, and consistent repeatability, particularly in nonlaboratory and resource-limited environments.