Development of the AI-Cirrhosis-ECG Score: An Electrocardiogram-Based Deep Learning Model in Cirrhosis.

Journal: The American journal of gastroenterology
PMID: 35029163

Abstract

INTRODUCTION: Cirrhosis is associated with cardiac dysfunction and distinct electrocardiogram (ECG) abnormalities. This study aimed to develop a proof-of-concept deep learning-based artificial intelligence (AI) model that could detect cirrhosis-related signals on ECG and generate an AI-Cirrhosis-ECG (ACE) score that would correlate with disease severity.

Authors

  • Joseph C Ahn
    Department of Medicine, Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MA, USA.
  • Zachi I Attia
    Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
  • Puru Rattan
    Department of Medicine, Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MA, USA.
  • Aidan F Mullan
    Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA.
  • Seth Buryska
    Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA.
  • Alina M Allen
    Department of Medicine, Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MA, USA.
  • Patrick S Kamath
    Department of Medicine, Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MA, USA.
  • Paul A Friedman
    Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
  • Vijay H Shah
    Department of Medicine, Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MA, USA.
  • Peter A Noseworthy
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
  • Douglas A Simonetto
    Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota.

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