Progress in radiomics of common heart disease based on cardiac magnetic resonance imaging

Fei, Jing-Le; Pu, Cailing; Xu, Fangyi; Wu, Yan; Hu, Hongjie

doi:10.31083/j.jmcm.2021.01.801

Cited by 3 publications

(1 citation statement)

References 56 publications

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“…Techniques such as texture analysis may be applied to pre-existing LGE segmentation to obtain features beyond quantitative scar burden, such as measures of heterogeneity and shape, which have been proven valuable in conventional workflows ( 119 , 133 ). Numerous extracted features and varying ML models have been evaluated for this role; in other words, ML can be used to identify novel predictors and implement novel prediction methods ( 134 , 135 ). However, these extracted features are typically unrecognizable to the human eye, especially when obtained from composite, multistep analyses necessitating further selection to identify covariates with the strongest predictive value ( 119 , 136 ).…”

Section: The Emerging Role Of Machine Learning and Artificial Intelli...mentioning

confidence: 99%

Advanced imaging for risk stratification for ventricular arrhythmias and sudden cardiac death

Xie

Sung

Saad

et al. 2022

Front. Cardiovasc. Med.

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Sudden cardiac death (SCD) is a leading cause of mortality, comprising approximately half of all deaths from cardiovascular disease. In the US, the majority of SCD (85%) occurs in patients with ischemic cardiomyopathy (ICM) and a subset in patients with non-ischemic cardiomyopathy (NICM), who tend to be younger and whose risk of mortality is less clearly delineated than in ischemic cardiomyopathies. The conventional means of SCD risk stratification has been the determination of the ejection fraction (EF), typically via echocardiography, which is currently a means of determining candidacy for primary prevention in the form of implantable cardiac defibrillators (ICDs). Advanced cardiac imaging methods such as cardiac magnetic resonance imaging (CMR), single-photon emission computerized tomography (SPECT) and positron emission tomography (PET), and computed tomography (CT) have emerged as promising and non-invasive means of risk stratification for sudden death through their characterization of the underlying myocardial substrate that predisposes to SCD. Late gadolinium enhancement (LGE) on CMR detects myocardial scar, which can inform ICD decision-making. Overall scar burden, region-specific scar burden, and scar heterogeneity have all been studied in risk stratification. PET and SPECT are nuclear methods that determine myocardial viability and innervation, as well as inflammation. CT can be used for assessment of myocardial fat and its association with reentrant circuits. Emerging methodologies include the development of “virtual hearts” using complex electrophysiologic modeling derived from CMR to attempt to predict arrhythmic susceptibility. Recent developments have paired novel machine learning (ML) algorithms with established imaging techniques to improve predictive performance. The use of advanced imaging to augment risk stratification for sudden death is increasingly well-established and may soon have an expanded role in clinical decision-making. ML could help shift this paradigm further by advancing variable discovery and data analysis.

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Section: The Emerging Role Of Machine Learning and Artificial Intelli...mentioning

confidence: 99%

Advanced imaging for risk stratification for ventricular arrhythmias and sudden cardiac death

Xie

Sung

Saad

et al. 2022

Front. Cardiovasc. Med.

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Cardiac magnetic resonance radiomics for disease classification

et al. 2022

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The beating heart: artificial intelligence for cardiovascular application in the clinic

Villegas-Martinez,

de Villedon de Naide,

Muthurangu

et al. 2024

Magn Reson Mater Phy

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Artificial intelligence (AI) integration in cardiac magnetic resonance imaging presents new and exciting avenues for advancing patient care, automating post-processing tasks, and enhancing diagnostic precision and outcomes. The use of AI significantly streamlines the examination workflow through the reduction of acquisition and postprocessing durations, coupled with the automation of scan planning and acquisition parameters selection. This has led to a notable improvement in examination workflow efficiency, a reduction in operator variability, and an enhancement in overall image quality. Importantly, AI unlocks new possibilities to achieve spatial resolutions that were previously unattainable in patients. Furthermore, the potential for low-dose and contrast-agent-free imaging represents a stride toward safer and more patient-friendly diagnostic procedures. Beyond these benefits, AI facilitates precise risk stratification and prognosis evaluation by adeptly analysing extensive datasets. This comprehensive review article explores recent applications of AI in the realm of cardiac magnetic resonance imaging, offering insights into its transformative potential in the field.

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Progress in radiomics of common heart disease based on cardiac magnetic resonance imaging

Cited by 3 publications

References 56 publications

Advanced imaging for risk stratification for ventricular arrhythmias and sudden cardiac death

Advanced imaging for risk stratification for ventricular arrhythmias and sudden cardiac death

Cardiac magnetic resonance radiomics for disease classification

The beating heart: artificial intelligence for cardiovascular application in the clinic

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