PARTNER: AI/ML-driven edge computing for cardiovascular disease diagnosis/mechanism study

Cuny City College, New York NY

Investigators

Abstract

This project is an ExpandAI Partnership between the City College of New York (CCNY) and the AI Institute for Future Edge Networks and Distributed Intelligence (AI-EDGE). In this project, a minority-serving institutions leads a new collaboration with an AI Institute focused on scaling up already-established AI research and education programs at CCNY and to pursue shared, complementary goals around developing AI with use for society in mind and for developing the next generation of AI education and workforce talent. The collaborative research focuses on the development of AI for the diagnosis of cardiovascular disease, which is a persistent leading cause of death in the U.S. The project will also build community and new centers of excellence in AI where such activities were not previously well developed. This project focuses on research towards a low-cost, easy-to-use, and high-precision sensing and learning system for the diagnosis of cardiovascular disease. To this end, the projects will take measurements from subjects using lightweight and safe multimodal sensors and analyze the data using artificial intelligence and machine learning. As a result, several cardiovascular parameters will be monitored in real time with personalized learning technologies. The research leverages the interdisciplinary expertise of CCNY and AI-EDGE on AI/ML edge computing, multimodal deep learning, medical computing, and computing-enabled disease mechanism study. An inexpensive and usable application of AI and edge computing technologies is envisioned with potential broader implications for further application of medical sensing, healthcare, and scientific research into the underlying molecular mechanisms of diseases. The findings and expertise gained from this project are expected to significantly facilitate the dissemination of applications of AI-enabled edge computing and distributed learning for healthcare. The project also features efforts to assist in community building in the neighborhood of CCNY to improve the outreach to under-represented minority communities and to offer AI and biomedical training opportunities for students from diverse groups. This project is co-funded by the NSF IUSE:HSI program, which has the goals of enhancing the quality of undergraduate STEM education, and increasing the recruitment, retention, and graduation rates of students pursuing associate’s or baccalaureate degrees in STEM. 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.