YOLOv5-aided paper-based microfluidic intelligent sensing platform for multiplex sweat biomarker analysis

Abstract

Sweat, a biofluid rich in various biomarkers, offers significant potential for non-invasive health monitoring and disease screening. Colorimetric detection is well-suited for multi-analyte quantification and point-of-care testing in sweat analysis, while conventional platforms often suffer from detection inaccuracies due to subjective interpretation and environmental interference. Furthermore, many existing systems rely on complex fabrication or computationally demanding artificial intelligence models, limiting their scalability and practical use in resource-limited settings. To address these challenges, we developed a YOLOv5-aided paper-based microfluidic intelligent sensing platform that integrates an easily fabricated paper-based microfluidic chip, smartphone imaging, and a deep learning framework which attains a mean average precision of 99.5%. This platform provides a cost-effective, portable, and reproducible approach for the detection of multiplex biomarkers in sweat, and its functionality has been successfully validated through the colorimetric analysis of iron ions, chloride ions, and glucose in sweat.