Deep learning artificial intelligence framework for multiclass coronary artery disease prediction using combination of conventional risk factors, carotid ultrasound, and intraplaque neovascularization.

Journal: Computers in biology and medicine
PMID: 36174330

Abstract

OBJECTIVE: Cardiovascular disease (CVD) is a major healthcare challenge and therefore early risk assessment is vital. Previous assessment techniques use either "conventional CVD risk calculators (CCVRC)" or machine learning (ML) paradigms. These techniques are ad-hoc, unreliable, not fully automated, and have variabilities. We, therefore, introduce AtheroEdge-MCDL (AE3.0) windows-based platform using multiclass Deep Learning (DL) system.

Authors

  • Amer M Johri
    Division of Cardiology, Department of Medicine, Queen's University, Kingston, ON, Canada.
  • Krishna V Singh
    Research Intern, AtheroPoint™, Roseville, CA, USA.
  • Laura E Mantella
    Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada.
  • Luca Saba
    Department of Radiology, A.O.U., Italy.
  • Aditya Sharma
    Division of Cardiovascular Medicine, University of Virginia, Charlottesville, 22901, VA, USA.
  • John R Laird
    UC Davis Vascular Center, University of California, Davis, CA, USA.
  • Kumar Utkarsh
    AtheroPoint™, Roseville, CA, USA.
  • Inder M Singh
    Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, 95747, CA, USA.
  • Suneet Gupta
    Department of Computer Science Engineering, Bennett University, India.
  • 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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