Gadgetron Global Network and Artificial Intelligence Powered Cardiac Imaging

去年，人工智能应用和数据管理有了新的发展。以下应用程序已开发并部署到临床扫描仪： .心脏灌注标测，基于AI的分割和报告 .心脏电影成像，基于AI的轮廓，用于后门控和自由呼吸成像 .基于AI的地标 .基于AI的全球纵向缩短（GLS）测量和报告 更多应用正在开发中： .基于AI的心肌T1和T2分割和报告 .使用AI从电影成像进行心脏功能量化和报告 除了开发更多的人工智能应用程序，未来的工作应该更多地关注人工智能战略，基础设施和临床价值的证据： . AI战略：如何开发一个系统来持续生产AI应用程序？如何确保开发的应用程序具有临床价值，除了出版物？如何确保临床合作者的参与来指导AI开发？ .基础设施：支持管理具有不同来源/类型/格式的大量数据，支持管理标记数据，支持迭代培训/测试/部署，确保道德合规并实现国际合作 .证据：收集证据的方法和工具，证明人工智能应用程序实际上作为临床工作流程的一部分产生价值，例如更好的成像效率（例如每天成像更多的患者）和更好或新颖的诊断。我们想证明人工智能不仅仅是花哨的研究。

Past year saw new development in AI applications and data curation. Following applications had been developed and deployed to clinical scanners: . Cardiac perfusion mapping with AI based segmentation and reporting . Cardiac cine imaging with AI based contouring, for retro-gated and free-breathing imaging . AI based landmarking . AI based measurement and reporting of global longitudinal shortening (GLS) More applications are under development: . AI based segmentation and reporting of myocardial T1 and T2 . Cardiac function quantification and reporting from cine imaging using AI Besides developing more AI applications, future work should focus more on AI strategy, infrastructure and evidences for clinical value: . AI strategy: how to develop a system to keep producing AI applications? How to ensure the developed applications have clinical value, besides making publications? How to ensure the involvement of clinical collaborators to guide AI development? . Infrastructure: to support curation of significant amount of data with different sources/types/formats, to support curation of labelled data, to support iterative training/testing/deployment, to ensure ethical compliance and enable international collaboration . Evidences: methods and tools to collect proof that AI applications actually generate values as a part of clinical workflow, e.g. better imaging efficiency (e.g. imaging more patients per day) and better or novel diagnosis. We want to show AI is more than just fancy research.