Diagnostic accuracy of 3D deep-learning-based fully automated estimation of patient-level minimum fractional flow reserve from coronary computed tomography angiography.

Journal: European heart journal. Cardiovascular Imaging
Published Date: Apr 1, 2020

Abstract

AIMS: Although deep-learning algorithms have been used to compute fractional flow reserve (FFR) from coronary computed tomography angiography (CCTA), no study has achieved 'fully automated' (i.e. free from human input) FFR calculation using deep-learning algorithms. The purpose of the study was to evaluate the accuracy of a fully automated 3D deep-learning model for estimating minimum FFR from CCTA data, with invasive FFR as the reference standard.

Authors

  • Kanako K Kumamaru
    From the Applied Imaging Science Laboratory, Department of Radiology, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115 (T.C., A.A.G., K.K.K., F.J.R., D.M.); Harvard T.H. Chan School of Public Health, Boston, Mass (S.Y.); and Department of Radiology, Brigham and Women's Hospital, Boston, Mass (T.K., B.R.).
  • Shinichiro Fujimoto
    Department of Cardiovascular Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan.
  • Yujiro Otsuka
    Department of Radiology, Juntendo University School of Medicine.
  • Tomohiro Kawasaki
    Department of Cardiology, Cardiovascular Center, Shin-Koga Hospital, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan.
  • Yuko Kawaguchi
    Department of Cardiovascular Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan.
  • Etsuro Kato
    Department of Cardiovascular Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan.
  • Kazuhisa Takamura
    Department of Cardiovascular Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan.
  • Chihiro Aoshima
    Department of Cardiovascular Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan.
  • Yuki Kamo
    Department of Cardiovascular Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan.
  • Yosuke Kogure
    Department of Radiological Technology, Juntendo University Hospital, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan.
  • Hidekazu Inage
    Department of Radiological Technology, Juntendo University Hospital, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan.
  • Hiroyuki Daida
    Department of Radiology Technology, Juntendo University Faculty of Health Science, Tokyo, Japan.
  • Shigeki Aoki

Keywords

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