MAR 23, 2021 6:00 AM PDT

AI Test Distinguishes Cancer Cells From Healthy Ones Based on Acidity Levels

WRITTEN BY: Tara Fernandes

Researchers have developed a new way of differentiating cancer cells from healthy ones—by how acidic they are. The novel diagnostic leverages artificial intelligence to provide answers in around half an hour, with a 95 percent accuracy rate.

“The ability to analyze single cells is one of the holy grails of health innovation for precision medicine or personalized therapy. Our proof-of-concept study demonstrates the potential of our technique to be used as a fast, inexpensive and accurate tool for cancer diagnosis,” explained professor Lim Chwee Tech, one of the test’s inventors from the National University of Singapore.

The test works like this: First, a pH-sensitive dye called bromothymol blue is applied onto patients’ cells maintained in cell culture dishes. Contact with the dye triggers a color change in cells, which varies depending on their acidity levels. Next, microscopes capture images of the cells. A machine-learning algorithm then analyzes the images and generates a report.

The pH inside cancer cells tends to be higher than that of healthy cells. This phenomenon occurs at the very early phases of cancer development and becomes amplified as it progresses.

“Unlike other cell analysis techniques, our approach uses simple, inexpensive equipment and does not require lengthy preparation and sophisticated devices,” said Lim, who added that artificial intelligence helps with boosting the screen’s accuracy and speed. “Furthermore, we can monitor and analyze living cells without causing any toxicity to the cells or the need to kill them. This would allow for further downstream analysis that may require live cells," Lim added.

The team is now exploring the potential of using the same technology to detect cancer cells suspended in blood. “One potential application for this would be in liquid biopsy where tumor cells that escaped from a primary tumor can be isolated in a minimally-invasive fashion from bodily fluids such as blood,” Lim commented.


Sources: NUS News, APL Bioengineering


About the Author
Doctorate (PhD)
Interested in health technology and innovation.
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