SBIR Phase I: Development of novel artificial intelligence (AI)-enabled, non-invasive, heart attack diagnostics

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is a novel, portable, diagnostic device for non-invasively diagnosing myocardial infarctions (MI) and ischemia in real-time with a high level of sensitivity and specificity. The system aims to provide an accurate point-of-care clinical classification for the 805,000 heart attack cases occurring in the US each year with a real-time diagnostic and monitoring tool. The system aims to significantly reduce the time needed with current invasive sampling and blood analysis measures, thereby improving patient outcomes during the first critical hours of an MI while saving healthcare resources and improving efficiency. The portable nature of the technology enables other forms of integration including at home, in clinic, hospital bedside use, or field use applications. This Small Business Innovation Research (SBIR) Phase I project will develop a proof-of-concept diagnostic-prognostic machine learning-based system for detecting MI. The scope of activities includes testing multiple techniques and models and producing distribution analyses and event plots of training data in order to optimize performance compared to clinically adjudicated events. Success measures include a data model which, when using their proprietary external noninvasive transdermal biomarker sensor with their wearable garment electrocardiograms (EKGs), are able to detect MI with > 85% accuracy. The device will also be able to detect clinically relevant ischemia with > 85% accuracy. These results will progress the company’s objective of completing a standalone, noninvasive, real-time diagnostic and remote monitoring tool. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.