A team of researchers at the George R. Brown School of Engineering and Computing at Rice University has developed an innovative artificial intelligence (AI)-enabled, low-cost device that will make flow cytometry ⎯ a technique used to analyze cells or particles in a fluid using a laser beam ⎯ affordable and accessible.
The prototype identifies and counts cells from unpurified blood samples with similar accuracy as the more expensive and bulky conventional flow cytometers, provides results within minutes and is significantly cheaper and compact, making it highly attractive for point-of-care clinical applications, particularly in low-resource and rural areas.
Peter Lillehoj, the Leonard and Mary Elizabeth Shankle Associate Professor of Bioengineering, and Kevin McHugh, assistant professor of bioengineering and chemistry, led the development of this new device. The study was published in Microsystems and Nanoengineering.
First developed in the 1950s, flow cytometry is a powerful technique for sorting and analyzing single cells that has applications in multiple medical fields including immunology, molecular and cancer biology and virology. It is the “gold standard” lab test for clinical diagnosis and care and is used extensively in biomedical research. However, its use is currently limited to state-of-the-art diagnostic labs and medical centers since it requires large, expensive equipment ranging from tens to hundreds of thousands of dollars and specially trained staff to operate it.
Conventional flow cytometry is not practical for many resource-limited settings in the U.S. and around the globe. With our approach, this technique can be performed with ease for a fraction of the cost.