Stephen Chou, a professor of electrical engineering at Princeton University and founder of Monmouth Park-based blood and health care testing company Essenlix Corp., has been part of a team developing an iPhone technology that can run a complete blood count.
Essenlix’s Instant Mobile Self-Test, or iMOST, uses an app, iPhone, iPhone attachment and a thin plastic cartridge for the sample. The attachment sits over the iPhone’s camera and flash.
To use the iMOST, a patient first pricks their finger, wipes away the initial drop of blood and then captures the second drop on the cartridge. The cartridge is then loaded into the attachment, which runs the test and gives the results.
Chou said Essenlix has raised about $20 million toward the development of iMOST, which has been years in the making.
The Princeton professor envisions the device being less expensive than current tests that require a patient to use laboratory services, and foresees the device measuring the number of white blood cells, red blood cells and hemoglobin, all by using an iPhone.
“Our device is 100 or 200 times cheaper than the big [hematology] machines,” Chou said. “Furthermore, we do not need a professional to operate it. We can get results quickly without an appointment. That is completely changing the whole dynamic.”
Chou said the test will be used to monitor overall health and it also is frequently used in blood cancer practices to check a patient’s blood count before chemotherapy.
Essenlix has applied to the U.S. Food and Drug Administration seeking approval to use the iMOST in human diagnostics. Chou could not say when he expects a decision from the FDA.
Chou said he believes iMOST will bring blood-testing technology to more people who might not have access to a full lab, such as in rural areas that may not have access to traditional, expensive equipment. And because the test can be run rather simply, it could lead to lower lab costs.
“To make the product successful, we need something else that is not in my expertise so we have a very strong team of people doing artificial intelligence and the machinery,” Chou said.
“The conventional testing today is based on the precision paradigm. … We are using an adaptive paradigm, which allows you to get an accurate result even if your measuring conditions are not perfect.”