This research introduces an artificial intelligence-assisted wearable microfluidic colorimetric sensor system (AI-WMCS) for rapid, non-invasive, and simultaneous detection of key biomarkers in human tears, including vitamin C, H+(pH), Ca2+, and proteins. The system integrates a flexible microfluidic epidermal patch with a deep-learning neural network-based cloud server data analysis system (CSDAS) embedded in a smartphone. A multichannel convolutional recurrent neural network (CNN-GRU) corrects errors in concentration data due to varying pH and color temperature. The AI-WMCS system achieves high accuracy in simultaneously detecting four critical tear biomarkers using only a small tear sample (~20 µL).
