Team 9: Aeris

This project introduces Aeris, a portable, cost-effective, and non-invasive diagnostic tool that analyzes an exhaled breath to detect acetone—a key biomarker correlated with diabetes—to facilitate early detection and screening. By integrating gas chromatography and a high-sensitivity photoionization detector, the system detects diabetes-associated volatile organic compounds (VOCs) at low parts-per-billion concentrations. The collected data is processed using a machine learning model to classify diagnostic outcomes, with results displayed via an intuitive user interface for healthcare professionals. Designed for affordability, ease of use, and rapid screening, Aeris aims to complement existing diagnostic approaches by identifying high-risk patients for further testing, ultimately improving health outcomes in underserved communities.