A Deep Learning Approach for Assessment of Regional Wall Motion Abnormality From Echocardiographic Images.

Journal: JACC. Cardiovascular imaging
Published Date: May 15, 2019

Abstract

OBJECTIVES: This study investigated whether a deep convolutional neural network (DCNN) could provide improved detection of regional wall motion abnormalities (RWMAs) and differentiate among groups of coronary infarction territories from conventional 2-dimensional echocardiographic images compared with that of cardiologists, sonographers, and resident readers.

Authors

  • Kenya Kusunose
    Department of Cardiovascular Medicine, Tokushima University Hospital, Tokushima, Japan. Electronic address: kusunosek@tokushima-u.ac.jp.
  • Takashi Abe
    Department of Information Engineering, Faculty of Engineering, Niigata University.
  • Akihiro Haga
  • Daiju Fukuda
    Department of Cardiovascular Medicine, Tokushima University Hospital, Tokushima, Japan.
  • Hirotsugu Yamada
    Department of Cardiovascular Medicine, Tokushima University Hospital, Tokushima, Japan.
  • Masafumi Harada
    Department of Radiology, Tokushima University Hospital, 2-50-1, Kuramoto-cho, Tokushima City, Tokushima, 770-8503, Japan.
  • Masataka Sata
    Department of Cardiovascular Medicine, Tokushima University Hospital, Tokushima, Japan.

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