Electrocardiogram-based deep learning algorithm for the screening of obstructive coronary artery disease.

Journal: BMC cardiovascular disorders
Published Date:

Abstract

BACKGROUND: Information on electrocardiogram (ECG) has not been quantified in obstructive coronary artery disease (ObCAD), despite the deep learning (DL) algorithm being proposed as an effective diagnostic tool for acute myocardial infarction (AMI). Therefore, this study adopted a DL algorithm to suggest the screening of ObCAD from ECG.

Authors

  • Seong Huan Choi
    Department of Cardiology, School of Medicine, Inha University Hospital, Inha University, Incheon, Republic of Korea.
  • Hyun-Gye Lee
    School of Medicine, Inha University, Incheon, Korea.
  • Sang-Don Park
    Department of Cardiology, School of Medicine, Inha University Hospital, Inha University, Incheon, Korea.
  • Jang-Whan Bae
    Department of Internal Medicine, College of Medicine, Chungbuk National University, Cheongju, Chungbuk, South Korea.
  • Woojoo Lee
    From the Department of Radiology, Seoul National University Bundang Hospital, 300 Gumi-dong, Bundang-gu, Seongnam-si, Gyeonggi-do 13620, Korea (S.J., H.S., Junghoon Kim, Jihang Kim, K.W.L., S.S.L., K.H.L.); Department of Radiology, Konkuk University Medical Center, Seoul, Korea (Y.J.S.); Seoul National University College of Medicine, Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea (K.W.L.); Department of Public Health Science, Graduate School of Public Health, Seoul National University, Seoul, Korea (W.L.); and Program in Biomedical Radiation Sciences, Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea (S.L.).
  • Mi-Sook Kim
    Department of Radiation Oncology, Korea Institute of Radiological & Medical Sciences, Seoul, Republic of Korea.
  • Tae-Hun Kim
    Department of Artificial Intelligence, Inha University, Incheon, Korea.
  • Won Kyung Lee
    Department of Information and Industrial Engineering, Yonsei University, 134 Shinchon-dong, Seoul 120-749, Republic of Korea.