Deep Learning Prediction for Distal Aortic Remodeling After Thoracic Endovascular Aortic Repair in Stanford Type B Aortic Dissection.

Journal: Journal of endovascular therapy : an official journal of the International Society of Endovascular Specialists
PMID: 36927177

Abstract

PURPOSE: This study aimed to develop a deep learning model for predicting distal aortic remodeling after proximal thoracic endovascular aortic repair (TEVAR) in patients with Stanford type B aortic dissection (TBAD) using computed tomography angiography (CTA).

Authors

  • Min Zhou
    Department of Respiratory and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
  • Xiaoyuan Luo
    Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China.
  • Xia Wang
    Department of Neurology, The Sixth People's Hospital of Huizhou City, Huizhou, China.
  • Tianchen Xie
    Department of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
  • Yonggang Wang
    Department of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
  • Zhenyu Shi
    Department of Vascular Surgery, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
  • Manning Wang
    Digital Medical Research Center, Fudan University, Shanghai, China.
  • Weiguo Fu
    Department of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai 200032, China.

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