Imaging for the diagnosis of acute myocarditis: can artificial intelligence improve diagnostic performance?

被引:0
|
作者
Shyam-Sundar, Vijay [1 ,2 ]
Harding, Daniel [1 ,2 ]
Khan, Abbas [3 ,4 ]
Abdulkareem, Musa [1 ]
Slabaugh, Greg [3 ,4 ]
Mohiddin, Saidi A. [1 ,2 ]
Petersen, Steffen E. [1 ,2 ,3 ]
Aung, Nay [1 ,2 ,3 ]
机构
[1] Queen Mary Univ London, William Harvey Res Inst, London, England
[2] St Bartholomews Hosp, Barts Heart Ctr, London, England
[3] Queen Mary Univ London, Digital Environm Res Inst, London, England
[4] Queen Mary Univ London, Sch Elect Engn & Comp Sci, London, England
来源
FRONTIERS IN CARDIOVASCULAR MEDICINE | 2024年 / 11卷
关键词
machine learning; artificial intelligence; cardiac MRI; myocarditis; diagnosis; LATE GADOLINIUM ENHANCEMENT; CARDIAC MAGNETIC-RESONANCE; SUSPECTED MYOCARDITIS; TRACKING; INFLAMMATION; ASSOCIATION;
D O I
10.3389/fcvm.2024.1408574
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Myocarditis is a cardiovascular disease characterised by inflammation of the heart muscle which can lead to heart failure. There is heterogeneity in the mode of presentation, underlying aetiologies, and clinical outcome with impact on a wide range of age groups which lead to diagnostic challenges. Cardiovascular magnetic resonance (CMR) is the preferred imaging modality in the diagnostic work-up of those with acute myocarditis. There is a need for systematic analytical approaches to improve diagnosis. Artificial intelligence (AI) and machine learning (ML) are increasingly used in CMR and has been shown to match human diagnostic performance in multiple disease categories. In this review article, we will describe the role of CMR in the diagnosis of acute myocarditis followed by a literature review on the applications of AI and ML to diagnose acute myocarditis. Only a few papers were identified with limitations in cases and control size and a lack of detail regarding cohort characteristics in addition to the absence of relevant cardiovascular disease controls. Furthermore, often CMR datasets did not include contemporary tissue characterisation parameters such as T1 and T2 mapping techniques, which are central to the diagnosis of acute myocarditis. Future work may include the use of explainability tools to enhance our confidence and understanding of the machine learning models with large, better characterised cohorts and clinical context improving the diagnosis of acute myocarditis.
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页数:10
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