Prediction of obstructive coronary artery disease using coronary calcification and epicardial adipose tissue assessments from CT calcium scoring scans.

Journal: Journal of cardiovascular computed tomography
PMID: 39909764

Abstract

BACKGROUND: Low-cost/no-cost non-contrast CT calcium scoring (CTCS) exams can provide direct evidence of coronary atherosclerosis. In this study, using features from CTCS images, we developed a novel machine learning model to predict obstructive coronary artery disease (CAD), as defined by the coronary artery disease-reporting and data system (CAD-RADS).

Authors

  • Juhwan Lee
    Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA.
  • Tao Hu
    Department of Preventive Dentistry, State Key Laboratory of Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China.
  • Michelle C Williams
    British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Chancellor's Building, 49 Little France Cres, Edinburgh, UK.
  • Ammar Hoori
  • Hao Wu
    Zhejiang Institute of Tianjin University (Shaoxing), Shaoxing, China.
  • Justin N Kim
    Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA.
  • David E Newby
    Edinburgh Imaging Facility QMRI, Edinburgh, EH16 4TJ, UK; Centre for Cardiovascular Science, Edinburgh, EH16 4TJ, UK.
  • Robert Gilkeson
    University Hospitals Cleveland Medical Center, Department of Radiology, Cleveland, OH, USA.
  • Sanjay Rajagopalan
    Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA.
  • David L Wilson
    Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106 and Department of Radiology, Case Western Reserve University, Cleveland, Ohio 44106.

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