A hybrid deep learning paradigm for carotid plaque tissue characterization and its validation in multicenter cohorts using a supercomputer framework.

Journal: Computers in biology and medicine
PMID: 34922173

Abstract

BACKGROUND: Early and automated detection of carotid plaques prevents strokes, which are the second leading cause of death worldwide according to the World Health Organization. Artificial intelligence (AI) offers automated solutions for plaque tissue characterization. Recently, solo deep learning (SDL) models have been used, but they do not take advantage of the tandem connectivity offered by AI's hybrid nature. Therefore, this study explores the use of hybrid deep learning (HDL) models in a multicenter framework, making this study the first of its kind.

Authors

  • Sanagala S Skandha
    CSE Department, CMR College of Engineering & Technology, Hyderabad, India; CSE Department, Bennett University, Greater Noida, UP, India.
  • Andrew Nicolaides
    Vascular Screening and Diagnostic Centre, London, England, United Kingdom; Vascular Diagnostic Center, University of Cyprus, Nicosia, Cyprus.
  • Suneet K Gupta
    Department of Computer Science Engineering, Bennett University, India.
  • Vijaya K Koppula
    CSE Department, CMR College of Engineering & Technology, Hyderabad, India.
  • Luca Saba
    Department of Radiology, A.O.U., Italy.
  • Amer M Johri
    Division of Cardiology, Department of Medicine, Queen's University, Kingston, ON, Canada.
  • Manudeep S Kalra
    Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA.
  • Jasjit S Suri
    Advanced Knowledge Engineering Center, Global Biomedical Technologies, Inc., Roseville, CA, USA. Electronic address: jsuri@comcast.net.

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