A Standalone and Portable Microfluidic Imaging Detection System With Embedded Computing for Point-of-Care Diagnostics

The existing imaging flow cytometers for cell detection usually require special optical microscope systems, high-speed cameras, and precision injection pumps, which are not applicable for point-of-care (POC) diagnostics. Recently, there are some portable cell imaging detection systems proposed, however, most of them just miniaturize the imaging part yet still rely on external personal computers to realize the system control, image storage, processing, and display. To address these challenges, we propose a standalone and portable microfluidic imaging detection system with embedded computing. The fully self-contained system integrates a portable microfluidic image acquisition module, a light source module, an embedded computing and control module, a micropump module, a touch control panel module, and a power supply module to realize bio-sample detection, processing, and result display functions with 1.9 kg weight and 20(L) x 10(W) x 15(H) cm(3) size. Experimental results showed that the system could clearly image human blood cells, tumor cells, and microspheres of different sizes. Under the flow rate of 1 mu L/min, the classification and counting accuracy of the system can reach over 99.7% for 10 mu m and 15 mu m particles in either single or mixed microbead sample solution, and reach 95.96% for HepG2 (Human hepatocellular carcinomas) cells in mixed cell and microbead sample solution. This system can automatically collect and analyze cells without being in the laboratory, which enables a shortened time-to-result cell detection approach. Therefore, we believe that the portable cell analysis equipment could provide a promising solution for future POC diagnostics.

Automated summary

The article introduces a standalone and portable microfluidic imaging detection system with embedded computing, which enables autonomous cell detection and analysis. Experimental results show that the system can accurately image and count human blood cells, tumor cells, and microspheres, and can automatically collect and analyze cells in a shorter time, providing a promising solution for future point-of-care diagnostics.