A machine learning-based pulmonary venous obstruction prediction model using clinical data and CT image.

Journal: International journal of computer assisted radiology and surgery
Published Date: Apr 1, 2021

Abstract

PURPOSE: In this study, we try to consider the most common type of total anomalous pulmonary venous connection and established a machine learning-based prediction model for postoperative pulmonary venous obstruction by using clinical data and CT images jointly.

Authors

  • Zeyang Yao
    School of Medicine, South China University of Technology Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Dongchuan Rd 96, Guangzhou, 510080, China.
  • Xinrong Hu
    Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Dongchuan Rd 96, Guangzhou, 510080, China.
  • Xiaobing Liu
    Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Dongchuan Rd 96, Guangzhou, 510080, China.
  • Wen Xie
    Department of Plant Protection, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China. Electronic address: xiewen@caas.cn.
  • Yuhao Dong
    Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, PR China; Shantou University Medical College, Guangdong, PR China.
  • Hailong Qiu
    Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Dongchuan Rd 96, Guangzhou, 510080, China.
  • Zewen Chen
    Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Dongchuan Rd 96, Guangzhou, 510080, China.
  • Yiyu Shi
    University of Notre Dame.
  • Xiaowei Xu
    Department of Information Science, University of Arkansas, Little Rock, Arkansas, United States of America.
  • Meiping Huang
    Department of Catheterization Lab, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, People's Republic of China.
  • Jian Zhuang
    Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510100, China.

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