Deep learning with convolutional neural network for estimation of the characterisation of coronary plaques: Validation using IB-IVUS.

Journal: Radiography (London, England : 1995)
Published Date: Aug 14, 2021

Abstract

INTRODUCTION: Deep learning approaches have shown high diagnostic performance in image classifications, such as differentiation of malignant tumors and calcified coronary plaque. However, it is unknown whether deep learning is useful for characterizing coronary plaques without the presence of calcification using coronary computed tomography angiography (CCTA). The purpose of this study was to compare the diagnostic performance of deep learning with a convolutional neural network (CNN) with that of radiologists in the estimation of coronary plaques.

Authors

  • T Masuda
    Department of Radiological Technology, Faculty of Health Science and Technology, Kawasaki University of Medical Welfare, 288 Matsushima, Kurashiki-city, Okayama 701-0193, Japan. Electronic address: takanorimasuda@yahoo.co.jp.
  • T Nakaura
    Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1 Honjo, Kumamoto 860-8556, Japan.
  • Y Funama
    Department of Medical Physics, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan.
  • S Oda
    Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1 Honjo, Kumamoto 860-8556, Japan.
  • T Okimoto
    Department of Cardiovascular Internal Medicine, Tsuchiya General Hospital, Nakajima-cho 3-30, Naka-ku, Hiroshima 730-8655, Japan.
  • T Sato
    Department of Diagnostic Radiology, Tsuchiya General Hospital, Nakajima-cho 3-30, Naka-ku, Hiroshima 730-8655, Japan.
  • N Noda
    Department of Radiological Technologist, Medical Corporation JR Hiroshima Hospital, Hiroshima, Japan.
  • T Yoshiura
    Department of Radiological Technology, Tsuchiya General Hospital, Nakajima-cho 3-30, Naka-ku, Hiroshima 730-8655, Japan.
  • Y Baba
    Saitama Medical University International Medical Center, 1397-1, Yamane, Hidaka-City, Saitama-Pref, 350-1298, Japan.
  • S Arao
    Department of Radiological Technology, Faculty of Health Science and Technology, Kawasaki University of Medical Welfare, 288 Matsushima, Kurashiki-city, Okayama 701-0193, Japan.
  • J Hiratsuka
    Department of Radiological Technology, Faculty of Health Science and Technology, Kawasaki University of Medical Welfare, 288 Matsushima, Kurashiki-city, Okayama 701-0193, Japan.
  • K Awai
    Department of Diagnostic Radiology, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan.

Keywords

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