Deep Learning Review Roadmap Cardiovascular Magnetic Resonance Artificial Intelligence Clinical Translation

External authors:

• Qiang Zhang ( University of Oxford , Florida International University )
• Anastasia Fotaki ( King's College London , Guy's & St Thomas' NHS Foundation Trust )
• Sona Ghadimi ( University of Virginia )
• Yu Wang ( University of Virginia )
• Mariya Doneva ( Philips )
• Jens Wetzl ( Siemens Healthineers AG )
• Jana G. Delfino ( US Food & Drug Administration (FDA) )
• Declan P. O'Regan ( Imperial College London )
• Frederick H. Epstein ( University of Virginia )

Abstract:

Background: Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment of heart disease; however, limitations of CMR include long exam times and high complexity compared to other cardiac imaging modalities. Recently advancements in artificial intelligence (AI) technology have shown great potential to address many CMR limitations. While the developments are remarkable, translation of AI-based methods into real-world CMR clinical practice remains at a nascent stage and much work lies ahead to realize the full potential of AI for CMR. Methods: Herein we review recent cutting-edge and representative examples demonstrating how AI can advance CMR in areas such as exam planning, accelerated image reconstruction, post-processing, quality control, classification and diagnosis. Results: These advances can be applied to speed up and simplify essentially every application including cine, strain, late gadolinium enhancement, parametric mapping, 3D whole heart, flow, perfusion and others. AI is a unique technology based on training models using data. Beyond reviewing the literature, this paper discusses important AI-specific issues in the context of CMR, including specialIntscript properties and characteristics of datasets for training and validation, specialIntscript previously published guidelines for reporting CMR AI research, specialIntscript considerations around clinical deployment, specialIntscript responsibilities of clinicians and the need for multi-disciplinary teams in the development and deployment of AI in CMR, specialIntscript industry considerations, and specialIntscript regulatory perspectives. Conclusions: Understanding and consideration of all these factors will contribute to the effective and ethical deployment of AI to improve clinical CMR.