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

该项目是纽约城市学院(CCNY)和AI未来边缘网络和分布式智能研究所(AI-EDGE)之间的扩展AI合作伙伴关系。在这个项目中，一个为少数群体服务的机构领导着与一个人工智能研究所的新合作，该研究所专注于扩大CCNY已经建立的人工智能研究和教育项目，并围绕发展人工智能以及培养下一代人工智能教育和劳动力人才寻求共同的、互补的目标。合作研究的重点是开发用于心血管疾病诊断的人工智能，这是美国持续领先的死亡原因。该项目还将在人工智能领域建立社区和新的卓越中心，这些中心以前没有得到很好的发展。本项目致力于研究一种低成本、易用、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式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，几个心血管参数将通过个性化学习技术进行实时监测。这项研究利用了CCNY和AI-EDGE在AI/ML边缘计算、多模式深度学习、医学计算和计算启用的疾病机制研究方面的跨学科专业知识。人工智能和边缘计算技术的廉价和可用的应用被设想为对医疗传感、医疗保健和对疾病潜在分子机制的科学研究的进一步应用具有潜在的更广泛的影响。从这个项目中获得的发现和专业知识预计将极大地促进医疗保健领域人工智能边缘计算和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，以改善对代表性不足的少数族裔社区的外联，并为不同群体的学生提供人工智能和生物医学培训机会。该项目由NSF IUSE：HSI项目共同资助，该项目的目标是提高STEM本科教育的质量，并提高STEM专科或学士学位学生的招生、留校率和毕业率。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.