The role of artificial intelligence in predicting outcomes by cardiovascular magnetic resonance: A comprehensive systematic review

Assadi, Hosamadin ORCID:, Alabed, Samer, Maiter, Ahmed, Salehi, Mahan, Li, Rui, Ripley, David P., van der Geest, Rob J., Zhong, Yumin, Zhong, Liang, Swift, Andrew J. and Garg, Pankaj ORCID: (2022) The role of artificial intelligence in predicting outcomes by cardiovascular magnetic resonance: A comprehensive systematic review. Medicina, 58 (8). ISSN 1648-9144

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Background and Objectives: Interest in artificial intelligence (AI) for outcome prediction has grown substantially in recent years. However, the prognostic role of AI using advanced cardiac magnetic resonance imaging (CMR) remains unclear. This systematic review assesses the existing literature on AI in CMR to predict outcomes in patients with cardiovascular disease. Materials and Methods: Medline and Embase were searched for studies published up to November 2021. Any study assessing outcome prediction using AI in CMR in patients with cardiovascular disease was eligible for inclusion. All studies were assessed for compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Results: A total of 5 studies were included, with a total of 3679 patients, with 225 deaths and 265 major adverse cardiovascular events. Three methods demonstrated high prognostic accuracy: (1) three-dimensional motion assessment model in pulmonary hypertension (hazard ratio (HR) 2.74, 95%CI 1.73-4.34, p < 0.001), (2) automated perfusion quantification in patients with coronary artery disease (HR 2.14, 95%CI 1.58-2.90, p < 0.001), and (3) automated volumetric, functional, and area assessment in patients with myocardial infarction (HR 0.94, 95%CI 0.92-0.96, p < 0.001). Conclusion: There is emerging evidence of the prognostic role of AI in predicting outcomes for three-dimensional motion assessment in pulmonary hypertension, ischaemia assessment by automated perfusion quantification, and automated functional assessment in myocardial infarction.

Item Type: Article
Additional Information: Funding: P.G. and A.J.S. are funded by Wellcome Trust Clinical Research Career Development Fellowships (220703/Z/20/Z & 205188/Z/16/Z). The funders had no role in study design, data collection and analysis, publishing decisions, or manuscript preparation. Rights retention statement: For the purpose of Open Access, these authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.
Uncontrolled Keywords: artificial intelligence,machine learning,cmr,systematic review,prognosis,machine learning,systematic review,artificial intelligence,medicine(all),sdg 3 - good health and well-being ,/dk/atira/pure/subjectarea/asjc/2700
Faculty \ School: Faculty of Medicine and Health Sciences > Norwich Medical School
UEA Research Groups: Faculty of Medicine and Health Sciences > Research Centres > Metabolic Health
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Depositing User: LivePure Connector
Date Deposited: 15 Aug 2022 08:30
Last Modified: 15 Jul 2024 01:07
DOI: 10.3390/medicina58081087


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