Artificial intelligence-enhanced electrocardiogram for the diagnosis of cardiac amyloidosis: A systemic review and meta-analysis.

Journal: Current problems in cardiology
PMID: 39306149

Abstract

BACKGROUND: Diagnosis of cardiac amyloidosis (CA) is often delayed due to variability in clinical presentation. The electrocardiogram (ECG) is one of the most common and widely available tools for assessing cardiovascular diseases. Artificial intelligence (AI) models analyzing ECG have recently been developed to detect CA, but their pooled accuracy is yet to be evaluated.

Authors

  • Laibah Arshad Khan
    Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA.
  • Fahad Hassan Shaikh
    Mercy St Vincent Medical Center, Toledo, OH, USA.
  • Muhammad Sami Khan
    Department of Medicine, Calderdale and Huddersfield NHS Foundation Trust, Halifax, United Kingdom. Electronic address: msami96@gmail.com.
  • Bayan Zafar
    Dow Medical College, Dow University of Health Sciences, Karachi, Pakistan.
  • Maheera Farooqi
    Dow Medical College, Dow University of Health Sciences, Karachi, Pakistan.
  • Bayarbaatar Bold
    Mongolian Society of Artificial Intelligence in Medicine, Ulaanbaatar, Mongolia.
  • Hafiza Madiha Aslam
    Mohtarma Benazir Bhutto Shaheed Medical College, Mirpur, Azad Jammu Kashmir, Pakistan.
  • Nabeeha Essam
    Jinnah Sindh Medical University, Karachi, Pakistan.
  • Isma Noor
    West Suffolk Hospital NHS Foundation Trust, United Kingdom.
  • Amber Siddique
    Faisalabad Medical Univeristy, Faisalabad, Pakistan.
  • Saad Shakil
    Islamabad Medical and Dental College, Islamabad, Pakistan.
  • Mahnoor Asghar Keen
    Khyber Medical College, Peshawar, Pakistan.

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