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Ischemia and outcome prediction by cardiac CT based machine learning

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Abstract

Cardiac CT using non-enhanced coronary artery calcium scoring (CACS) and coronary CT angiography (cCTA) has been proven to provide excellent evaluation of coronary artery disease (CAD) combining anatomical and morphological assessment of CAD for cardiovascular risk stratification and therapeutic decision-making, in addition to providing prognostic value for the occurrence of adverse cardiac outcome. In recent years, artificial intelligence (AI) and, in particular, the application of machine learning (ML) algorithms, have been promoted in cardiovascular CT imaging for improved decision pathways, risk stratification, and outcome prediction in a more objective, reproducible, and rational manner. AI is based on computer science and mathematics that are based on big data, high performance computational infrastructure, and applied algorithms. The application of ML in daily routine clinical practice may hold potential to improve imaging workflow and to promote better outcome prediction and more effective decision-making in patient management. Moreover, CT represents a field wherein ML may be particularly useful, such as CACS and cCTA. Thus, the purpose of this review is to give a short overview about the contemporary state of ML based algorithms in cardiac CT, as well as to provide clinicians with currently available scientific data on clinical validation and implementation of these algorithms for the prediction of ischemia-specific CAD and cardiovascular outcome.

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Abbreviations

AI:

Artificial intelligence

AUC:

Area under the curve

CACS:

Coronary artery calcium scoring

CAD:

Coronary artery disease

cCTA:

Coronary CT angiography

CFD:

Computational fluid dynamics

CT-FFR:

CT-derived fractional flow reserve

CTP:

CT myocardial perfusion

ICA:

Invasive coronary angiography

MACE:

Major adverse cardiac events

ML:

Machine learning

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Authors and Affiliations

  1. Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Heart & Vascular Center, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA

    Verena Brandt, Tilman Emrich, U. Joseph Schoepf, Danielle M. Dargis, Richard R. Bayer & Christian Tesche

  2. Department of Cardiology, Robert-Bosch-Krankenhaus, Stuttgart, Germany

    Verena Brandt

  3. Department of Diagnostic and Interventional Radiology, University Medical Center Mainz, Mainz, Germany

    Tilman Emrich

  4. German Center for Cardiovascular Research (DZHK), Partner Site Rhine-Main, Mainz, Germany

    Tilman Emrich

  5. Division of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences, Emory University Hospital, Emory Healthcare, Inc., Atlanta, GA, USA

    Carlo N. De Cecco

  6. Department of Cardiology, Munich University Clinic, Ludwig-Maximilians-University, Munich, Germany

    Christian Tesche

  7. Department of Internal Medicine, St. Johannes–Hospital, Dortmund, Germany

    Christian Tesche

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  1. Verena Brandt
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  2. Tilman Emrich
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Correspondence to U. Joseph Schoepf.

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Conflict of interest

UJ.S. and C.N.D.C. receive institutional research support and/or honoraria for speaking and consulting from Bayer, Bracco, Elucid BioImaging, Guerbet, HeartFlow Inc., and Siemens Healthineers. C.T. receives honoraria for speaking and consulting from HeartFlow Inc. and Siemens Healthineers.

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Brandt, V., Emrich, T., Schoepf, U.J. et al. Ischemia and outcome prediction by cardiac CT based machine learning. Int J Cardiovasc Imaging 36, 2429–2439 (2020). https://doi.org/10.1007/s10554-020-01929-y

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