Editorial: Current and Future Role of Artificial Intelligence in Cardiac Imaging

Lekadir, Karim; Leiner, Tim; Young, Alistair A.; Petersen, Steffen E.

doi:10.3389/fcvm.2020.00137

Cited by 10 publications

(12 citation statements)

References 13 publications

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“…Machine learning methods offer simplification and acceleration of the diagnostic path of MI and are useful as a guide for treatment strategies [ 88 ]. Baessler et al used CMR images with contrast as a reference to differentiate chronic from subacute MI on noncontrast CMR images, while Zhang et al directly used noncontrast CMR images to diagnose chronic MI [ 74 , 85 ].…”

Section: Diagnosismentioning

confidence: 99%

“…Machine learning is an excellent method that allows for the differentiation of various cardiomyopathies [ 88 ]. Gopalakrishnan et al used CMR parameters of the left ventricle, right ventricle, and overall heart from a cohort of 83 pediatric subjects in order to characterize five different cardiomyopathies: HCM, DCM, ARVC, left ventricle noncompaction, and myocarditis [ 86 ].…”

Section: Diagnosismentioning

confidence: 99%

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The Applications of Artificial Intelligence in Cardiovascular Magnetic Resonance—A Comprehensive Review

Argentiero

Muscogiuri

Rabbat

et al. 2022

JCM

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Cardiovascular disease remains an integral field on which new research in both the biomedical and technological fields is based, as it remains the leading cause of mortality and morbidity worldwide. However, despite the progress of cardiac imaging techniques, the heart remains a challenging organ to study. Artificial intelligence (AI) has emerged as one of the major innovations in the field of diagnostic imaging, with a dramatic impact on cardiovascular magnetic resonance imaging (CMR). AI will be increasingly present in the medical world, with strong potential for greater diagnostic efficiency and accuracy. Regarding the use of AI in image acquisition and reconstruction, the main role was to reduce the time of image acquisition and analysis, one of the biggest challenges concerning magnetic resonance; moreover, it has been seen to play a role in the automatic correction of artifacts. The use of these techniques in image segmentation has allowed automatic and accurate quantification of the volumes and masses of the left and right ventricles, with occasional need for manual correction. Furthermore, AI can be a useful tool to directly help the clinician in the diagnosis and derivation of prognostic information of cardiovascular diseases. This review addresses the applications and future prospects of AI in CMR imaging, from image acquisition and reconstruction to image segmentation, tissue characterization, diagnostic evaluation, and prognostication.

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Section: Diagnosismentioning

confidence: 99%

Section: Diagnosismentioning

confidence: 99%

The Applications of Artificial Intelligence in Cardiovascular Magnetic Resonance—A Comprehensive Review

Argentiero

Muscogiuri

Rabbat

et al. 2022

JCM

View full text Add to dashboard Cite

show abstract

“…Use of artificial intelligence continues to proliferate in imaging generally and it will undoubtedly impact SSc care. Already deep learning techniques are being used to aid in tissue segmentation and identification of fibrosis ( 98 ). Given the prominent role fibrosis plays in SSc and particularly arrhythmia risk, automated techniques may allow for earlier identification of at risk individuals.…”

Section: Future Directionsmentioning

confidence: 99%

“…Given the prominent role fibrosis plays in SSc and particularly arrhythmia risk, automated techniques may allow for earlier identification of at risk individuals. Algorithms are being developed to aid in motion and deformation pattern analysis ( 98 ), which may allow for earlier detection of subtle abnormalities, especially in the geometrically complex RV. Finally, learning algorithms combining both clinical and imaging may be helpful in guiding treatment selection and predicting response ( 98 ).…”

Section: Future Directionsmentioning

confidence: 99%

“…Algorithms are being developed to aid in motion and deformation pattern analysis ( 98 ), which may allow for earlier detection of subtle abnormalities, especially in the geometrically complex RV. Finally, learning algorithms combining both clinical and imaging may be helpful in guiding treatment selection and predicting response ( 98 ). Given the complexity of the disease and its multiple treatment options, SSc may prove a fertile ground for these types of studies.…”

Section: Future Directionsmentioning

confidence: 99%

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Cardiovascular Imaging for Systemic Sclerosis Monitoring and Management

Glynn

Hale

Hussain

et al. 2022

Front. Cardiovasc. Med.

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Systemic sclerosis (SSc) is a complex connective tissue disease with multiple clinical and subclinical cardiac manifestations. SSc can affect most structural components of the heart, including the pericardium, myocardium, valves, and conduction system through a damaging cycle of inflammation, ischemia, and fibrosis. While cardiac involvement is the second leading SSc-related cause of death, it is frequently clinically silent in early disease and often missed with routine screening. To facilitate identification of cardiac disease in this susceptible population, we present here a review of cardiac imaging modalities and potential uses in the SSc patient population. We describe well-characterized techniques including electrocardiography and 2D echocardiography with Doppler, but also discuss more advanced imaging approaches, such as speckle-tracking echocardiography, cardiovascular magnetic resonance imaging (CMR), and stress imaging, among others. We also suggest an algorithm for the appropriate application of these modalities in the workup and management of patients with SSc. Finally, we discuss future opportunities for cardiac imaging in SSc research to achieve early detection and to optimize treatment.

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Artificial Intelligence Algorithm Development for Biomedical Imaging

Ooijen

Oever

2022

Artificial Intelligence in Cardiothoracic Imaging

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Editorial: Current and Future Role of Artificial Intelligence in Cardiac Imaging

Cited by 10 publications

References 13 publications

The Applications of Artificial Intelligence in Cardiovascular Magnetic Resonance—A Comprehensive Review

The Applications of Artificial Intelligence in Cardiovascular Magnetic Resonance—A Comprehensive Review

Cardiovascular Imaging for Systemic Sclerosis Monitoring and Management

Artificial Intelligence Algorithm Development for Biomedical Imaging

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