The precise, simultaneous, and rapid detection of essential biomarkers in human tears is imperative for monitoring both ocular and systemic health. The utilization of a wearable colorimetric biochemical sensor exhibits potential in achieving swift and concurrent detection of pivotal biomarkers in tears. Nevertheless, challenges arise in the collection, interpretation, and sharing of data from the colorimetric sensor, thereby restricting the practical implementation of this technology. To overcome these challenges, 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 sensor consists of a flexible microfluidic epidermal patch that collects tears and facilitates the colorimetric reaction, and a deep-learning neural network-based cloud server data analysis system (CSDAS) embedded in a smartphone enabling color data acquisition, interpretation, auto-correction, and display. To enhance accuracy, a well-trained multichannel convolutional recurrent neural network (CNN-GRU) corrects errors in the interpreted concentration data caused by varying pH and color temperature in different measurements. The test set determination coefficients (R²) of 1D-CNN-GRU for predicting pH and 3D-CNN-GRU for predicting the other three biomarkers were as high as 0.998 and 0.994, respectively. This correction significantly improves the accuracy of the predicted concentration, enabling accurate, simultaneous, and quick detection of four critical tear biomarkers using only minute amounts of tears ( ~ 20 μL). This research demonstrates the powerful integration of a flexible microfluidic colorimetric biosensor and deep-learning algorithm, which holds immense potential to revolutionize the fields of health monitoring.

精确、同步、快速地检测人类泪液中的重要生物标志物对于监测眼部和全身健康至关重要。使用可穿戴比色生化传感器在快速、同时检测眼泪中的关键生物标志物方面具有潜力。然而，在比色传感器数据的收集、解释和共享方面出现了挑战，从而限制了该技术的实际实施。为了克服这些挑战，本研究引入了人工智能辅助的可穿戴微流体比色传感器系统（AI-WMCS），用于快速、非侵入性、同时检测人类眼泪中的关键生物标志物，包括维生素C、H ⁺ （pH）、 Ca ²⁺和蛋白质。该传感器由一个灵活的微流体表皮贴片组成，可收集眼泪并促进比色反应，以及嵌入智能手机中的基于深度学习神经网络的云服务器数据分析系统（CSDAS），可实现颜色数据采集、解释、自动校正、并显示。为了提高准确性，训练有素的多通道卷积循环神经网络 (CNN-GRU) 可以纠正由于不同测量中 pH 和色温变化而导致的解释浓度数据中的错误。用于预测pH的1D-CNN-GRU和用于预测其他三个生物标志物的3D-CNN-GRU的测试集判定系数（R ² ）分别高达0.998和0.994。这种校正显着提高了预测浓度的准确性，仅使用微量泪液（约 20 μL）即可准确、同步和快速地检测四种关键泪液生物标志物。 这项研究展示了灵活的微流体比色生物传感器和深度学习算法的强大集成，具有彻底改变健康监测领域的巨大潜力。