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Artificial intelligence in computed tomography plaque characterization: A review

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    Riccardo Cau
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    Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari – Polo di Monserrato, s.s. 554 Monserrato (Cagliari), 09045, Italy
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    Adam Flanders
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    Thomas Jefferson University, 1020 Walnut Street, Philadelphia, PA, United States
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    Lorenzo Mannelli
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    IRCCS Fondazione SDN, Naples, Italy
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    Carola Politi
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    1 All authors contributed equally as authors to this work.
    Affiliations
    Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari – Polo di Monserrato, s.s. 554 Monserrato (Cagliari), 09045, Italy
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    Gavino Faa
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    Department of Pathology, Azienda Ospedaliero Universitaria (AOU) di Cagliari, University Hospital San Giovanni di Dio, Cagliari, Italy

    Proteomic Laboratory - European Center for Brain Research, IRCCS Fondazione Santa Lucia, Rome, Italy
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    Jasjit S. Suri
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    Stroke Diagnosis and Monitoring Division ATHEROPOINT LLC, Roseville, CA USA
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    Luca Saba
    Correspondence
    Corresponding author.
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    1 All authors contributed equally as authors to this work.
    Affiliations
    Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari – Polo di Monserrato, s.s. 554 Monserrato (Cagliari), 09045, Italy
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      Highlights

      • Artificial intelligence in plaque characterization is promising for different tasks
      • Artificial intelligence models reduce variability and human workflow in plaque analysis.
      • Artificial intelligence algorithms help radiologists assess plaque morphology.

      Abstract

      Cardiovascular disease (CVD) is associated with high mortality around the world. Prevention and early diagnosis are key targets in reducing the socio-economic burden of CVD.
      Artificial intelligence (AI) has experienced a steady growth due to technological innovations that have to lead to constant development. Several AI algorithms have been applied to various aspects of CVD in order to improve the quality of image acquisition and reconstruction and, at the same time adding information derived from the images to create strong predictive models. In computed tomography angiography (CTA), AI can offer solutions for several parts of plaque analysis, including an automatic assessment of the degree of stenosis and characterization of plaque morphology. A growing body of evidence demonstrates a correlation between some type of plaques, so-called high-risk plaque or vulnerable plaque, and cardiovascular events, independent of the degree of stenosis. The radiologist must apprehend and participate actively in developing and implementing AI in current clinical practice.
      In this current overview on the existing AI literature, we describe the strengths, limitations, recent applications, and promising developments of employing AI to plaque characterization with CT.

      Abbreviations:

      CVD (cardiovascular disease), CTA (computed tomography angiography), MI (myocardial infarction), AI (artificial intelligence), ML (machine learning), DL (deep learning), PVAT (perivascular adipose tissue), FFR (fractional flow reserve)

      Keywords

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