SpiroSniff: A Machine Learning Driven Breathalyzer for Lung Cancer Detection

Abstract

SpiroSniff is a portable, affordable breathalyzer designed to detect early-stage lung cancer by analyzing volatile organic compounds (VOCs) in exhaled breath. Leveraging metal oxide chemiresistive sensors and machine learning algorithms, the device aims to provide a non-invasive, rapid, and accurate alternative to traditional diagnostic methods like CT scans and X-rays, which are often inaccessible and cost-prohibitive, especially in low-resource settings. With a production cost target of under $150 and a desired specificity and sensitivity of at least 80%, SpiroSniff is engineered for widespread use, including in underserved populations. The project addresses a critical need for early detection tools in light of the rising prevalence of respiratory diseases and the high mortality rate associated with undiagnosed lung cancer. By focusing on robust sensor selection, in-lab validation, algorithm development, and ethical deployment, SpiroSniff has the potential to transform global lung cancer diagnostics and improve public health outcomes.