A deep-learning system integrating electrocardiograms and laboratory indicators for diagnosing acute aortic dissection and acute myocardial infarction.

Journal: International journal of cardiology
PMID: 39880045

Abstract

BACKGROUND: Acute Stanford Type A aortic dissection (AAD-type A) and acute myocardial infarction (AMI) present with similar symptoms but require distinct treatments. Efficient differentiation is critical due to limited access to radiological equipment in many primary healthcare. This study develops a multimodal deep learning model integrating electrocardiogram (ECG) signals and laboratory indicators to enhance diagnostic accuracy for AAD-type A and AMI.

Authors

  • Liping Wang
    School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200011, China.
  • Hai Wu
    Institute of Forensic Science, Hunan Provincial Public Security Bureau, Changsha, 410001, People's Republic of China.
  • Chaoyong Wu
    School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 102488, China. Electronic address: wucy@bucm.edu.cn.
  • Lan Shu
    Quality Control Office, Zigong Fourth People's Hospital, Zigong, Sichuan 643000, China.
  • Dehao Zhou
    Department of Computer Center, Zigong Fourth People's Hospital, Zigong, Sichuan 643000, China.

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