Machine Learning Constructed Based on Patient Plaque and Clinical Features for Predicting Stent Malapposition: A Retrospective Study.

Journal: Clinical cardiology
PMID: 39119892

Abstract

BACKGROUND: Stent malapposition (SM) following percutaneous coronary intervention (PCI) for myocardial infarction continues to present significant clinical challenges. In recent years, machine learning (ML) models have demonstrated potential in disease risk stratification and predictive modeling.

Authors

  • Qianhang Xia
    Department of Cardiology, The Third Affiliated Hospital of Zunyi Medical University (The First People's Hospital of Zunyi), Zunyi, China.
  • Chancui Deng
    Department of Cardiology, Affiliated Hospital of Zunyi Medical University, Zunyi, China.
  • Shuangya Yang
    Department of Cardiology, Affiliated Hospital of Zunyi Medical University, Zunyi, China.
  • Ning Gu
    Department of Cardiology, Affiliated Hospital of Zunyi Medical University, Zunyi, China.
  • Youcheng Shen
    Department of Cardiology, Affiliated Hospital of Zunyi Medical University, Zunyi, China.
  • Bei Shi
    Program of Artificial Intelligence in Medicine, College of Medicine, Soochow University, Suzhou 215123, China.
  • Ranzun Zhao
    Program of Artificial Intelligence in Medicine, College of Medicine, Soochow University, Suzhou 215123, China.

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