Unraveling phenotypic heterogeneity in stanford type B aortic dissection patients through machine learning clustering analysis of cardiovascular CT imaging.

Journal: Hellenic journal of cardiology : HJC = Hellenike kardiologike epitheorese
PMID: 39128706

Abstract

OBJECTIVE: Aortic dissection remains a life-threatening condition necessitating accurate diagnosis and timely intervention. This study aimed to investigate phenotypic heterogeneity in patients with Stanford type B aortic dissection (TBAD) through machine learning clustering analysis of cardiovascular computed tomography (CT) imaging.

Authors

  • Kun Liu
    Department of Anesthesiology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai 200030, China.
  • Deyin Zhao
    Second Ward of General Surgery, Suzhou Municipal Hospital of Anhui Province, Suzhou, China.
  • Lvfan Feng
    Shanghai Health Development Research Center (Shanghai Medical Information Center), Shanghai, China.
  • Zhaoxuan Zhang
    School of Life and Pharmaceutical Sciences, Dalian University of Technology, Panjin, China.
  • Peng Qiu
  • Xiaoyu Wu
  • Ruihua Wang
    Neurology Department, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Azad Hussain
    Department of Mathematics, University of Gujrat, Gujrat, Pakistan.
  • Jamol Uzokov
    Cardiology Department, Republican Specialized Scientific Practical Medical Center of Therapy and Medical Rehabilitation, 4 Osiyo, 100084 Tashkent, Uzbekistan.
  • Yanshuo Han
    School of Life and Pharmaceutical Sciences, Dalian University of Technology, Panjin, China; Central Hospital of Dalian, University of Dalian, Dalian, China. Electronic address: yanshuohan@dlut.edu.cn.

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