Artificial intelligence for predicting shockable rhythm during cardiopulmonary resuscitation: In-hospital setting.

Journal: Resuscitation
PMID: 39029581

Abstract

AIM OF THE STUDY: This study aimed to develop an artificial intelligence (AI) model capable of predicting shockable rhythms from electrocardiograms (ECGs) with compression artifacts using real-world data from emergency department (ED) settings. Additionally, we aimed to explore the black box nature of AI models, providing explainability.

Authors

  • Sejoong Ahn
    Department of Emergency Medicine, Korea University Ansan Hospital, 15355 Ansan-si, Republic of Korea.
  • Sumin Jung
    Research Division, Heuron Co., Ltd, Incheon, South Korea.
  • Jong-Hak Park
    Department of Emergency Medicine, Korea University Ansan Hospital, 15355 Ansan-si, Republic of Korea.
  • Hanjin Cho
    Department of Emergency Medicine, Korea University Ansan Hospital, 15355 Ansan-si, Republic of Korea.
  • Sungwoo Moon
    Department of Emergency Medicine, Korea University Ansan Hospital, 15355 Ansan-si, Republic of Korea.
  • Sukyo Lee
    Department of Emergency Medicine, Korea University Ansan Hospital, 15355 Ansan-si, Republic of Korea. Electronic address: sukyolee@korea.ac.kr.

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