CRP Analysis Accelerated - Machine Learning in Paper Microfluidics

Abstract

In modern healthcare, rapid and accurate analysis of biomarkers like C-reactive protein (CRP) is crucial for timely disease diagnosis and monitoring. Conventional diagnostic methods often suffer from complexity and reliance on specialized equipment, limiting their applicability in point-of-care settings. Addressing this challenge, the integration of machine learning (ML) techniques into paper-based microfluidic devices offers a promising solution to accelerate CRP analysis. Paper-based microfluidics, characterized by affordability and portability, provides an ideal platform for decentralized healthcare. Leveraging the computational power of ML algorithms, our approach enables swift and precise CRP segmentation, reducing analysis time compared to traditional methods. Experimental validation demonstrates the efficacy of our methodology in achieving rapid and accurate CRP analysis across various sample concentrations and complexities. The integration of ML with paper microfluidics holds promise for advancing point-of-care diagnostics, supporting personalized treatment strategies, and facilitating early disease detection. CRP Analysis Accelerated - Machine Learning in Paper Microfluidics represents a transformative initiative at the intersection of artificial intelligence and biomedical engineering, with profound implications for improving global health outcomes.Top of Form