Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology View Full Text


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Article Info

DATE

2019-02

AUTHORS

Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti

ABSTRACT

To summarize the advances achieved in the detection and characterization of myocardial ischemia and prediction of related outcomes through machine learning (ML)-based artificial intelligence (AI) workflows in both single-photon emission computed tomography (SPECT) and positron emission tomography (PET). In the field of cardiology, the implementation of ML algorithms has recently gravitated around image processing for characterization, diagnostic, and prognostic purposes. Nuclear cardiology represents a particular niche for AI as it deals with complex images of semi-quantitative and quantitative nature acquired with SPECT and PET. AI is revolutionizing clinical research. Since the recent convergence of powerful ML algorithms and increasing computational power, the study of very large datasets has demonstrated that clinical classification and prediction can be optimized by exploring very high-dimensional non-linear patterns. In the evaluation of myocardial ischemia, ML is optimizing the recognition of perfusion abnormalities beyond traditional measures and refining prediction of adverse cardiovascular events at the individual-patient level. More... »

PAGES

5

References to SciGraph publications

  • 2017-12. Diagnostic accuracy of an artificial neural network compared with statistical quantitation of myocardial perfusion images: a Japanese multicenter study in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
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  • 2018-03-14. Prediction of cardiac death after adenosine myocardial perfusion SPECT based on machine learning in JOURNAL OF NUCLEAR CARDIOLOGY
  • 2001-10. Random Forests in MACHINE LEARNING
  • 2017-02. Dermatologist-level classification of skin cancer with deep neural networks in NATURE
  • 2017. Medical Image Synthesis with Context-Aware Generative Adversarial Networks in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION − MICCAI 2017
  • 2018-12. The machine learning horizon in cardiac hybrid imaging in EUROPEAN JOURNAL OF HYBRID IMAGING
  • 2017-10. Mastering the game of Go without human knowledge in NATURE
  • 2013-08. Improved accuracy of myocardial perfusion SPECT for detection of coronary artery disease by machine learning in a large population in JOURNAL OF NUCLEAR CARDIOLOGY
  • 2018-05-22. Machine learning in the integration of simple variables for identifying patients with myocardial ischemia in JOURNAL OF NUCLEAR CARDIOLOGY
  • 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2015
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s12410-019-9480-x

    DOI

    http://dx.doi.org/10.1007/s12410-019-9480-x

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1112040692


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