Machine learning detects symptomatic patients with carotid plaques based on 6-type calcium configuration classification on CT angiography.

Journal: European radiology
PMID: 37982835

Abstract

OBJECTIVES: While the link between carotid plaque composition and cerebrovascular vascular (CVE) events is recognized, the role of calcium configuration remains unclear. This study aimed to develop and validate a CT angiography (CTA)-based machine learning (ML) model that uses carotid plaques 6-type calcium grading, and clinical parameters to identify CVE patients with bilateral plaques.

Authors

  • Francesco Pisu
    Department of Radiology, Azienda Ospedaliero Universitaria, Monserrato, Cagliari, Italy.
  • Hui Chen
    Xiangyang Central HospitalAffiliated Hospital of Hubei University of Arts and Science Xiangyang 441000 China.
  • Bin Jiang
    Department of Urology, Chinese People's Liberation Army General Hospital, Beijing, 100039 China.
  • Guangming Zhu
    1 Department of Radiology, Neuroradiology Division, Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305.
  • Marco Virgilio Usai
    Department of Vascular Surgery, St. Franziskus Hospital, University of Münster, Münster, Germany.
  • Martin Austermann
    Department of Vascular Surgery, St. Franziskus Hospital, University of Münster, Münster, Germany.
  • Yousef Shehada
    Department of Vascular Surgery, St. Franziskus Hospital, University of Münster, Münster, Germany.
  • Elias Johansson
    Clinical Science, Neurosciences, Umeå University, Umeå, Sweden.
  • Jasjit Suri
    Global Biomedical Technologies Inc., Roseville, CA, USA.
  • Giuseppe Lanzino
    Department of Neurosurgery, Mayo Clinic, Rochester, MN, USA.
  • J C Benson
    Department of Radiology, Mayo Clinic, Rochester, MN, USA.
  • Valentina Nardi
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
  • Amir Lerman
    Department of Cardiovascular Diseases, Mayo Clinic College of Medicine, Rochester, Minnesota.
  • Max Wintermark
    Department of Radiology, Stanford University, Stanford, California, USA.
  • Luca Saba
    Department of Radiology, A.O.U., Italy.

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