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Clinical Application of AI-ECG

  • Zheng-Kai Xue,
  • Kang-Yin Chen,
  • Xin-Mu Li,
  • Tong Liu,
  • Jia-Wei Xie,
  • Shao-Hua Guo,
  • Wen-Hua Song,
  • Hui-Min Chu,
  • Guo-Hua Fu,
  • Ni-Xiao Zhang,
  • Bin Zhou,
  • Min Tang,
  • Bin-Hao Wang,
  • Bing-Xin Xie,
  • Guan-Yu Mu,
  • Peng Wang

摘要

The integration of AI into ECG signifies a pivotal shift in the cardiovascular domain. Recent years have borne witness to the advancements of ECG interpretation using AI techniques as a pivotal focus of research, including deep learning and CNN. Multi-layer AI networks excel in detecting intricate signals and patterns that often mimic human perception. A plethora of digitized clinical ECG datasets have provided the foundation for developing AI models, enabling the detection of paroxysmal atrial fibrillation, left ventricular dysfunction, cardiomyopathy, and phenotypes such as hyperkalemia and valve irregularities. This chapter encapsulates the present state of AI-ECG application in cardiovascular disease detection, deliberates its clinical significance, appraises potential limitations, and contemplates future prospects.