12 Apr 2023
NIST-JILA’s comb technology could also detect other diseases, such as COPD, lung cancer, and kidney failure.JILA (formerly the Joint Institute for Laboratory Astrophysics, part of the University of Colorado) have upgraded a breathalyzer based on Nobel Prize-winning frequency-comb technology and combined it with machine learning to detect SARS-CoV-2 infection in 170 volunteer subjects “with excellent accuracy”.
They say that their achievement “represents the first real-world test of the technology’s capability to diagnose disease in exhaled human breath.” The development is described in a paper in the Journal of Breath Research.
Frequency comb technology has the potential to non-invasively diagnose more health conditions than other breath analysis techniques while also being faster and potentially more accurate than some other medical tests.
Human breath contains more than 1,000 different trace molecules, many of which are correlated with specific health conditions. JILA’s frequency comb breathalyzer identifies chemical signatures of molecules based on exact wavelengths and amounts of infrared light absorbed by a sample of exhaled breath.
In 2008, Jun Ye and colleagues at JILA demonstrated the world’s first frequency comb breathalyzer, which measured the absorption of light in the near-infrared part of the optical spectrum.
Then in 2021 they achieved a thousandfold improvement in detection sensitivity by extending the technique to the mid-infrared spectral region, where molecules absorb light much more strongly. This enables some breath molecules to be identified at the parts-per-trillion level where those with the lowest concentrations tend to be present.
The added benefit to this study was the use of machine learning. Machine learning processes and analyzes a large volume of data from all the breath samples as measured by 14,836 comb “teeth,” each representing a different frequency to create a predictive model to diagnose disease.
NIST/JILA Fellows Jun Ye and David Nesbitt built a breathalyzer that identifies biomarkers of disease.
“Molecules increase or decrease in their concentrations when associated with specific health conditions. Machine learning analyzes this information, identifies patterns and develops reliable criteria we can use to predict a diagnosis,” said Qizhong Liang, a graduate student in the Jun Ye group, who is lead author of a new paper presenting the findings.
JILA is jointly operated by the U.S. National Institute of Standards and Technology (NIST) and the University of Colorado Boulder (CU Boulder). The research was conducted on breath samples collected from 170 CU Boulder students and staff from May 2021 to January 2022. Approximately half of the volunteers tested positive for Covid-19 with standard PCR tests. The other half of the subjects tested negative.
“I do think that this comb technique is superior to anything out there,” NIST/JILA Fellow Jun Ye said. “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.”
In the future, the researchers could further increase the accuracy by expanding the spectral coverage, analyzing the patterns with more powerful AI techniques, and measuring and analyzing additional molecules, which could include the SARS-CoV-2 virus itself.
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