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 ) and cholesterol (LOD: 217 ), 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.