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

Medicine and Health

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

Z. Wang, Y. Dong, et al.

Discover the groundbreaking AI-WMCS, a wearable microfluidic sensor system that rapidly and non-invasively detects critical biomarkers in human tears. Developed by Zihu Wang, Yan Dong, Xiaoxiao Sui, Xingyan Shao, Kangshuai Li, Hao Zhang, Zhenyuan Xu, and Dongzhi Zhang, this innovative technology offers high accuracy using just a tiny tear sample!

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Playback language: English
Abstract
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).
Publisher
npj Flexible Electronics
Published On
Jun 13, 2024
Authors
Zihu Wang, Yan Dong, Xiaoxiao Sui, Xingyan Shao, Kangshuai Li, Hao Zhang, Zhenyuan Xu, Dongzhi Zhang
Tags
AI-WMCS
biomarkers
wearable sensor
tear analysis
microfluidics
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