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

该项目是纽约城市学院（CCNY）与未来边缘网络和分布式智能人工智能研究所（AI- Edge）之间的扩展人工智能合作伙伴关系。在这个项目中，一个少数民族服务机构与一个人工智能研究所进行了新的合作，重点是扩大CCNY已经建立的人工智能研究和教育项目，并在开发人工智能方面追求共同的、互补的目标，为社会服务，并培养下一代人工智能教育和劳动力人才。这项合作研究的重点是开发用于诊断心血管疾病的人工智能，心血管疾病是美国长期以来的主要死亡原因。该项目还将在以前没有很好地开展此类活动的人工智能领域建立社区和新的卓越中心。本项目致力于研究一种低成本、易于使用、高精度的心血管疾病诊断传感和学习系统。为此，这些项目将使用轻型和安全的多模态传感器对受试者进行测量，并使用人工智能和机器学习分析数据。因此，一些心血管参数将通过个性化学习技术实时监测。该研究利用了CCNY和AI- edge在AI/ML边缘计算、多模态深度学习、医学计算和计算支持疾病机制研究方面的跨学科专业知识。预计人工智能和边缘计算技术的廉价和可用应用可能对医疗传感、医疗保健和疾病潜在分子机制的科学研究的进一步应用产生更广泛的影响。预计从该项目获得的成果和专门知识将大大促进基于人工智能的边缘计算和医疗保健分布式学习应用的传播。该项目还努力协助CCNY附近的社区建设，以改善与代表性不足的少数民族社区的联系，并为来自不同群体的学生提供人工智能和生物医学培训机会。本项目由美国国家科学基金会（NSF） IUSE:HSI项目共同资助，旨在提高STEM本科教育的质量，提高攻读STEM副学士学位或学士学位的学生的招聘、保留和毕业率。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.