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Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization

Author

Listed:
  • Anjan Gudigar

    (Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India)

  • Sneha Nayak

    (Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India)

  • Jyothi Samanth

    (Department of Cardiovascular Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal 576104, India)

  • U Raghavendra

    (Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India)

  • Ashwal A J

    (Department of Cardiology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal 576104, India)

  • Prabal Datta Barua

    (School of Management & Enterprise, University of Southern Queensland, Toowoomba, QLD 4350, Australia
    Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia)

  • Md Nazmul Hasan

    (Department of Cardiology, Ad-Din Medical College Hospital, Dhaka 1217, Bangladesh)

  • Edward J. Ciaccio

    (Department of Medicine, Division of Cardiology, Columbia University Medical Center, New York, NY 10032, USA)

  • Ru-San Tan

    (Department of Cardiology, National Heart Centre Singapore, Singapore 169609, Singapore
    Duke-NUS Medical School, Singapore 169857, Singapore)

  • U. Rajendra Acharya

    (School of Engineering, Ngee Ann Polytechnic, Clementi 599489, Singapore
    Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
    Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore S599494, Singapore)

Abstract

Coronary artery disease is a major cause of morbidity and mortality worldwide. Its underlying histopathology is the atherosclerotic plaque, which comprises lipid, fibrous and—when chronic—calcium components. Intravascular ultrasound (IVUS) and intravascular optical coherence tomography (IVOCT) performed during invasive coronary angiography are reference standards for characterizing the atherosclerotic plaque. Fine image spatial resolution attainable with contemporary coronary computed tomographic angiography (CCTA) has enabled noninvasive plaque assessment, including identifying features associated with vulnerable plaques known to presage acute coronary events. Manual interpretation of IVUS, IVOCT and CCTA images demands scarce physician expertise and high time cost. This has motivated recent research into and development of artificial intelligence (AI)-assisted methods for image processing, feature extraction, plaque identification and characterization. We performed parallel searches of the medical and technical literature from 1995 to 2021 focusing respectively on human plaque characterization using various imaging modalities and the use of AI-assisted computer aided diagnosis (CAD) to detect and classify atherosclerotic plaques, including their composition and the presence of high-risk features denoting vulnerable plaques. A total of 122 publications were selected for evaluation and the analysis was summarized in terms of data sources, methods—machine versus deep learning—and performance metrics. Trends in AI-assisted plaque characterization are detailed and prospective research challenges discussed. Future directions for the development of accurate and efficient CAD systems to characterize plaque noninvasively using CCTA are proposed.

Suggested Citation

  • Anjan Gudigar & Sneha Nayak & Jyothi Samanth & U Raghavendra & Ashwal A J & Prabal Datta Barua & Md Nazmul Hasan & Edward J. Ciaccio & Ru-San Tan & U. Rajendra Acharya, 2021. "Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization," IJERPH, MDPI, vol. 18(19), pages 1-27, September.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:19:p:10003-:d:641307
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    References listed on IDEAS

    as
    1. Zhifan Gao & Wei Guo & Xin Liu & Wenhua Huang & Heye Zhang & Ning Tan & William Kongto Hau & Yuan-Ting Zhang & Huafeng Liu, 2014. "Automated Detection Framework of the Calcified Plaque with Acoustic Shadowing in IVUS Images," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-18, November.
    2. Peter Libby, 2002. "Inflammation in atherosclerosis," Nature, Nature, vol. 420(6917), pages 868-874, December.
    3. David Moher & Alessandro Liberati & Jennifer Tetzlaff & Douglas G Altman & The PRISMA Group, 2009. "Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement," PLOS Medicine, Public Library of Science, vol. 6(7), pages 1-6, July.
    4. Roman Zeleznik & Borek Foldyna & Parastou Eslami & Jakob Weiss & Ivanov Alexander & Jana Taron & Chintan Parmar & Raza M. Alvi & Dahlia Banerji & Mio Uno & Yasuka Kikuchi & Julia Karady & Lili Zhang &, 2021. "Deep convolutional neural networks to predict cardiovascular risk from computed tomography," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    5. Vidhya V. & Anjan Gudigar & U. Raghavendra & Ajay Hegde & Girish R. Menon & Filippo Molinari & Edward J. Ciaccio & U. Rajendra Acharya, 2021. "Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives," IJERPH, MDPI, vol. 18(12), pages 1-29, June.
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    Cited by:

    1. Álvaro T. Latorre & Miguel A. Martínez & Myriam Cilla & Jacques Ohayon & Estefanía Peña, 2022. "Atherosclerotic Plaque Segmentation Based on Strain Gradients: A Theoretical Framework," Mathematics, MDPI, vol. 10(21), pages 1-20, October.

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