Machine learning enabled detection of COVID-19 pneumonia using exhaled breath analysis: a proof-of-concept study

This study demonstrates that a ML-based breathprint model using LAS analysis of exhaled breath may be a valuable non-invasive method for studying the lower airways and detecting SARS-CoV-2 and other respiratory pathogens. The technology and the ML a pproach can be easily deployed in any setting with minimal training. This will greatly improve access and scalability to meet surge capacity; allow early and rapid detection to inform therapy; and offers great versatility in developing new classifier models quickly for future outbreaks.

Source: Journal of Breath Research - March 13, 2024 Category: Respiratory Medicine Authors: Ruth P Cusack, Robyn Larracy, Christian B Morrell, Maral Ranjbar, Jennifer Le Roux, Christiane E Whetstone, Maxime Boudreau, Patrick F Poitras, Thiviya Srinathan, Eric Cheng, Karen Howie, Catie Obminski, Tim O ’Shea, Rebecca J Kruisselbrink, Terence Ho, Source Type: research