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

该项目是纽约城市学院（CCNY）和未来边缘网络和分布式智能人工智能研究所（AI-EDGE）之间的ExpandAI合作伙伴关系。在这个项目中，一个少数民族服务机构与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计划，并围绕开发人工智能实现共同的互补目标，同时考虑到社会的用途，并开发下一代人工智能教育和劳动力人才。该合作研究的重点是开发用于诊断心血管疾病的人工智能，这是美国持续的主要死亡原因，该项目还将在人工智能领域建立社区和新的卓越中心，这些活动以前没有得到很好的发展。 该项目的重点是研究一种低成本，易于使用，高精度的传感和学习系统，用于诊断心血管疾病。为此，这些项目将使用轻量级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，将通过个性化学习技术对多个心血管参数进行真实的实时监测。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且可用的应用被设想为对医学传感，医疗保健和科学研究进一步应用于疾病的潜在分子机制具有潜在的更广泛的影响。从该项目中获得的研究结果和专业知识预计将大大促进支持AI的边缘计算和分布式学习在医疗保健领域的应用的传播。该项目还致力于协助CCNY附近的社区建设，以改善对代表性不足的少数民族社区的宣传，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。该项目由NSF IUSE：HSI项目共同资助，其目标是提高本科STEM教育的质量，并提高攻读STEM副学士或学士学位学生的招聘，保留和毕业率。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估而被认为值得支持。

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.