Artificial intelligence assessment for early detection and prediction of renal impairment using electrocardiography.

Journal: International urology and nephrology
PMID: 35403974

Abstract

PURPOSE: Although renal failure is a major healthcare burden globally and the cornerstone for preventing its irreversible progression is an early diagnosis, an adequate and noninvasive tool to screen renal impairment (RI) reliably and economically does not exist. We developed an interpretable deep learning model (DLM) using electrocardiography (ECG) and validated its performance.

Authors

  • Joon-Myoung Kwon
    Department of Emergency Medicine, Mediplex Sejong Hospital, Incheon, 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.
  • Min-Seung Jung
    Medical Research Team, Medical AI Co. Ltd., Seoul, South Korea.
  • Yong-Hyeon Cho
    Medical Research Team, Medical AI Co. Ltd., Seoul, South Korea.
  • Jae-Hyun Shin
    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.
  • 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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