An artificial intelligence-assisted microfluidic colorimetric wearable sensor system for monitoring of key tear biomarkers  

作　　者：Zihu Wang Yan Dong Xiaoxiao Sui Xingyan Shao Kangshuai Li Hao Zhang Zhenyuan Xu Dongzhi Zhang 

机构地区：[1]College of Control Science and Engineering,China University of Petroleum(East China),Qingdao,China

出　　处：《npj Flexible Electronics》2024年第1期557-567,共11页npj-柔性电子(英文)

基　　金：funded by the National Natural Science Foundation of China(51777215);the Special Foundation of the Taishan Scholar Project(tsqn202211077,tsqn202311077);Shandong Provincial ExcellentOverseas Young Scholar Foundation(2023HWYQ-069);the Shandong Provincial Natural Science Foundation(ZR2023ME118);the Natural Science Foundation of Qingdao City(23-2-1-219-zyyd-jch,23-2-1-111-zyyd-jch);the Fundamental Research Funds for the Central Universities(23CX06032A).

摘　　要：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),Ca^(2+),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(R^(2))of 1D-CNN-GRU for predicting pH and 3DCNN-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(~20μL).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.

关 键 词：artificial WEAR system 

分 类 号：TP2[自动化与计算机技术—检测技术与自动化装置]