Artificial intelligence for detecting electrolyte imbalance using electrocardiography.

Journal: Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc
Published Date: May 1, 2021

Abstract

INTRODUCTION: The detection and monitoring of electrolyte imbalance is essential for appropriate management of many metabolic diseases; however, there is no tool that detects such imbalances reliably and noninvasively. In this study, we developed a deep learning model (DLM) using electrocardiography (ECG) for detecting electrolyte imbalance and validated its performance in a multicenter study.

Authors

  • Joon-Myoung Kwon
    Department of Emergency Medicine, Mediplex Sejong Hospital, Incheon, Korea.
  • Min-Seung Jung
    Medical Research Team, Medical AI Co. Ltd., Seoul, South Korea.
  • Kyung-Hee Kim
    Department of Cardiology, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Korea.
  • Yong-Yeon Jo
    Medical research team, Medical AI, Seoul, South Korea.
  • Jae-Hyun Shin
    Medical Research Team, Medical AI Co. Ltd., Seoul, South Korea.
  • Yong-Hyeon Cho
    Medical Research Team, Medical AI Co. Ltd., Seoul, South Korea.
  • Yoon-Ji Lee
    Medical Research Team, Medical AI Co. Ltd., Seoul, South Korea.
  • Jang-Hyeon Ban
    Medical R&D Center, Bodyfriend Co. Ltd., Seoul, South Korea.
  • Ki-Hyun Jeon
    Department of Cardiology, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Korea.
  • Soo Youn Lee
    Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul 110799, South Korea.
  • Jinsik Park
    Department of Cardiology, Mediplex Sejong Hospital, Incheon, Korea.
  • Byung-Hee Oh
    Division of Cardiology, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Republic of Korea.

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