CAD-RADS scoring of coronary CT angiography with Multi-Axis Vision Transformer: A clinically-inspired deep learning pipeline.

Journal: Computer methods and programs in biomedicine
Published Date: Dec 21, 2023

Abstract

BACKGROUND AND OBJECTIVE: The standard non-invasive imaging technique used to assess the severity and extent of Coronary Artery Disease (CAD) is Coronary Computed Tomography Angiography (CCTA). However, manual grading of each patient's CCTA according to the CAD-Reporting and Data System (CAD-RADS) scoring is time-consuming and operator-dependent, especially in borderline cases. This work proposes a fully automated, and visually explainable, deep learning pipeline to be used as a decision support system for the CAD screening procedure. The pipeline performs two classification tasks: firstly, identifying patients who require further clinical investigations and secondly, classifying patients into subgroups based on the degree of stenosis, according to commonly used CAD-RADS thresholds.

Authors

  • Alessia Gerbasi
    Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 5, Pavia, Italy. Electronic address: alessia.gerbasi01@universitadipavia.it.
  • Arianna Dagliati
    1 Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
  • Giuseppe Albi
    Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 5, Pavia, Italy.
  • Mattia Chiesa
    Centro Cardiologico Monzino, IRCCS, Milan, Italy.
  • Daniele Andreini
    Division of Cardiology and Cardiac Imaging, IRCCS Ospedale Galeazzi - Sant'Ambrogio Milan, Italy.
  • Andrea Baggiano
    Centro Cardiologico Monzino, IRCCS, Milan, Italy.
  • Saima Mushtaq
    Centro Cardiologico Monzino Scientific Institute for Research, Hospitalisation and Health Care (IRCCS) Milan Italy.
  • Gianluca Pontone
    Department of Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy.
  • Riccardo Bellazzi
    Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
  • Gualtiero Colombo
    School of Computer Science & Informatics, Cardiff University, UK.

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