Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence—review of evidence and proposition of a roadmap to clinical translation
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...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | Journal of Cardiovascular Magnetic Resonance |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1097664724010780 |
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| author | Qiang Zhang Anastasia Fotaki Sona Ghadimi Yu Wang Mariya Doneva Jens Wetzl Jana G. Delfino Declan P. O’Regan Claudia Prieto Frederick H. Epstein |
| author_facet | Qiang Zhang Anastasia Fotaki Sona Ghadimi Yu Wang Mariya Doneva Jens Wetzl Jana G. Delfino Declan P. O’Regan Claudia Prieto Frederick H. Epstein |
| author_sort | Qiang Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4029bda83a2e4d6e963b32c19994dd3f |
| institution | Kabale University |
| issn | 1097-6647 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Cardiovascular Magnetic Resonance |
| spelling | doaj-art-4029bda83a2e4d6e963b32c19994dd3f2024-12-16T05:34:38ZengElsevierJournal of Cardiovascular Magnetic Resonance1097-66472024-01-01262101051Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence—review of evidence and proposition of a roadmap to clinical translationQiang Zhang0Anastasia Fotaki1Sona Ghadimi2Yu Wang3Mariya Doneva4Jens Wetzl5Jana G. Delfino6Declan P. O’Regan7Claudia Prieto8Frederick H. Epstein9Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK; Big Data Institute, University of Oxford, Oxford, UKSchool of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK; Royal Brompton Hospital, Guy’s and St Thomas’ NHS Foundation Trust, London, UKDepartment of Biomedical Engineering, University of Virginia, Charlottesville, VA, USADepartment of Biomedical Engineering, University of Virginia, Charlottesville, VA, USAPhilips Innovative Technologies, Hamburg, GermanySiemens Healthineers AG, Erlangen, GermanyUS Food and Drug Administration, Center for Devices and Radiological Health (CDRH), Office of Science and Engineering Laboratories (OSEL), Silver Spring, MD, USAMRC Laboratory of Medical Sciences, Imperial College London, London, UKSchool of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK; School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile; Corresponding author. School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK.Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA; Corresponding author. Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.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.http://www.sciencedirect.com/science/article/pii/S1097664724010780Cardiovascular magnetic resonanceArtificial intelligenceDeep learningClinical translationReviewRoadmap |
| spellingShingle | Qiang Zhang Anastasia Fotaki Sona Ghadimi Yu Wang Mariya Doneva Jens Wetzl Jana G. Delfino Declan P. O’Regan Claudia Prieto Frederick H. Epstein Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence—review of evidence and proposition of a roadmap to clinical translation Journal of Cardiovascular Magnetic Resonance Cardiovascular magnetic resonance Artificial intelligence Deep learning Clinical translation Review Roadmap |
| title | Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence—review of evidence and proposition of a roadmap to clinical translation |
| title_full | Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence—review of evidence and proposition of a roadmap to clinical translation |
| title_fullStr | Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence—review of evidence and proposition of a roadmap to clinical translation |
| title_full_unstemmed | Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence—review of evidence and proposition of a roadmap to clinical translation |
| title_short | Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence—review of evidence and proposition of a roadmap to clinical translation |
| title_sort | improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence review of evidence and proposition of a roadmap to clinical translation |
| topic | Cardiovascular magnetic resonance Artificial intelligence Deep learning Clinical translation Review Roadmap |
| url | http://www.sciencedirect.com/science/article/pii/S1097664724010780 |
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