Gadgetron Global Network and Artificial Intelligence Powered Cardiac Imaging

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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.