Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence-review of evidence and proposition of a roadmap to clinical translation
File(s)
Published version
OA Location
Author(s)
Type
Journal Article
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 (1) properties and characteristics of datasets for training and validation, (2) previously published guidelines for reporting CMR AI research, (3) considerations around clinical deployment, (4) responsibilities of clinicians and the need for multi-disciplinary teams in the development and deployment of AI in CMR, (5) industry considerations, and (6) regulatory perspectives.
Conclusions
Understanding and consideration of all these factors will contribute to the effective and ethical deployment of AI to improve clinical CMR.
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 (1) properties and characteristics of datasets for training and validation, (2) previously published guidelines for reporting CMR AI research, (3) considerations around clinical deployment, (4) responsibilities of clinicians and the need for multi-disciplinary teams in the development and deployment of AI in CMR, (5) industry considerations, and (6) regulatory perspectives.
Conclusions
Understanding and consideration of all these factors will contribute to the effective and ethical deployment of AI to improve clinical CMR.
Date Issued
2024-11-01
Date Acceptance
2024-06-18
Citation
Journal of Cardiovascular Magnetic Resonance, 2024, 26 (2)
ISSN
1097-6647
Publisher
Society for Cardiovascular Magnetic Resonance
Journal / Book Title
Journal of Cardiovascular Magnetic Resonance
Volume
26
Issue
2
Copyright Statement
© 2024 The Authors. Published by Elsevier Inc. on behalf of Society for Cardiovascular Magnetic Resonance. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/38909656
PII: S1097-6647(24)01078-0
Subjects
ANGIOGRAPHY
Artificial intelligence
Cardiac & Cardiovascular Systems
CARDIOMYOPATHY
Cardiovascular magnetic resonance
Cardiovascular System & Cardiology
Clinical translation
Deep learning
DIAGNOSIS
LATE GADOLINIUM ENHANCEMENT
Life Sciences & Biomedicine
MOTION CORRECTION
MRI
MYOCARDIAL-INFARCTION
NEURAL-NETWORK
Radiology, Nuclear Medicine & Medical Imaging
REGISTRATION
Review
Roadmap
Science & Technology
Publication Status
Published
Coverage Spatial
England
Article Number
101051
Date Publish Online
2024-06-22