Picomole's breath analysis technologies will change the way disease is detected. Our best in class breath sampler can be used for a variety of applications related to gas collection. Our breath spectrometer creates unique composite biomarker for each indication (disease fingerprint). Picomole has developed machine learning software that analyzes individual composite biomarkers to detect disease. Picomole's research is focused on developing machine learning models for multiple indications.
As Picomole further develops its Indications Menu, more diseases will be subject to early detection which will improve the lives of future patients. Timely screening allows practitioners to identify health issues earlier, leads to saving healthcare resources, but more importantly, saving lives.
We have developed a user-friendly, portable breath sampler. Picomole's breath sampler is the world leading technology for alveolar breath sampling. Breath samples are collected on industry standard sorbent tubes.
Some applications of our breath sampler include, but are not limited to, breath collection, collection of industrial head space gasses and collection of air samples. Our breath sampler can be used in conjunction with any form of Mass Spectrometry, or any other technology that requires an input of an industry standard sorbent tube.
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Robust and user friendly. Picomole's technologies represent revolutionary advances in the emerging field of breath analytics
Picomole's spectrometer uses Cavity Ring Down Spectroscopy (CRDS) to generate unique, individual composite biomarkers. These composite biomarkers are analyzed using machine learning software to identify disease.
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Machine Learning Software
Picomole's machine learning software takes an individual's composite biomarker and identifies the differences between the individual's sample and a healthy baseline. The difference between the individuals composite biomarker and healthy baseline is used to determine the presence or absence of disease. We are committed to continually developing new machine learning models for new disease indications. To explore some of our possible indications, visit our Indications Menu.