Researchers at JILA have modified a breathalyzer to make it accurately detect COVID-19.
The researchers upgraded a breathalyzer that uses Nobel Prize-winning frequency-comb technology and combined it with machine learning to make it capable of accurately detecting SARS-CoV-2 infections in 170 volunteer subjects, according to an April 6 National Institute of Standards and Technology news release. The experiment marked the first real-world test of the technology's ability to diagnose diseases through breath analyses.
“I do think that this comb technique is superior to anything out there,” NIST/JILA Fellow Jun Ye said in the release. “The basic point is not just the detection sensitivity, but the fact that we can generate a far greater amount of data, or breath markers, really establishing a whole new field of ‘comb breathomics’ with the help of AI. With a database, we can then use it to search and study many other physiological conditions for human beings and to help advance the future of healthcare.”
Frequency comb technology is capable of non-invasively diagnosing more health conditions than other breath analysis techniques while also being faster and possibly more accurate than many other medical tests, the release said. Such combs work by acting as rulers for precisely measuring different colors of light, including the infrared light absorbed by molecules.
JILA's advancement in breath analysis technology could possibly revolutionize disease diagnosis by offering a non-invasive, fast and accurate method for detecting various health conditions through breath analysis, according to the release.
The researchers plan further studies to diagnose other conditions such as chronic obstructive pulmonary disease, which the World Health Organization lists as the third leading cause of death in the world, the release said.
They have also recently boosted the comb breathalyzer's diagnostic power by expanding the spectral coverage to identify additional molecules, the release reported. The researchers plan to utilize AI approaches such as deep learning to improve disease-detection abilities.
Efforts are currently underway to miniaturize and simplify the technology, making it portable and easy to use in hospitals and other care settings, according to the release.
