STTR Phase I: User-Friendly Spirometer and Mobile App for Self-Management and Home Monitoring of Asthma Patients

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project will close the gap between the hospital and home for proper asthma care. The greatest hurdles for proper lung assessment are costly devices ranging from $1000 to $30,000, and mandatory oversight from a skilled lab technician to ensure proper use. These hospital lung function tests allow for early detection of declining lung performance up to several days in advance of visible symptoms like coughing or wheezing, but no option for reliable assessment in the home currently exists. The proposed technology incorporates machine learning into an easy to use mobile-app that replicates a trained lab technician's coaching. In combination with an affordable consumer device that measures lung function, the technology makes proper lung assessment accessible outside the hospital, for regular tracking between office visits supported with physician-guided suggestions for reducing unnecessary and costly emergency visits and hospitalizations. The proposed project offers a preventative care solution for 10 million children with asthma in the US alone. This solution entails an engaging mobile-app game controlled by a handheld device that measures lung capacity, which together provide parents with an action plan to prevent their child?s asthma symptoms before they occur. Guidelines will be developed in consultation with pediatric pulmonologists to gain results as reliable as tests conducted under the guidance of a respiratory therapist in the hospital. A large database of expert labeled lung measurements will be used to train and test the neural network model to detect and decipher feature patterns and correlations. If imprecise information is collected, the app's machine-learned algorithm will determine the cause of failure and suggest a corrective action for the next attempt. This ensures that only properly collected lung measurements trigger recommendations for action. By the end of this proposal period, the applicant will have a machine learning algorithm on a mobile-app that matches the efficacy of in-person coaching by a trained lab technician, as well as evaluate the feasibility of the proposed technology for Phase II considerations.