Predicting Acute Exacerbation Phenotype in Chronic Obstructive Pulmonary Disease Patients Using VGG-16 Deep Learning.

Journal: Respiration; international review of thoracic diseases
Published Date:

Abstract

INTRODUCTION: Exacerbations of chronic obstructive pulmonary disease (COPD) have a significant impact on hospitalizations, morbidity, and mortality of patients. This study aimed to develop a model for predicting acute exacerbation in COPD patients (AECOPD) based on deep-learning (DL) features.

Authors

  • Shengchuan Feng
    State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China, fengsc2022@163.com.
  • Ran Zhang
    Jiangsu Province Key Laboratory of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing, Jiangsu, 210009, China.
  • Wenxiu Zhang
    Institute of Research and Clinical Innovations, Neusoft Medical Systems Co., Ltd., Shanghai, China.
  • Yuqiong Yang
    State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Aiqi Song
    Nanshan School, Guangzhou Medical University, Guangzhou, China.
  • Jiawei Chen
  • Fengyan Wang
    iFLYTEK Research, Hefei, China.
  • Jiaxuan Xu
    Faculty of Engineering and Applied Science, University of Regina, Regina, SK, S4S 0A2, Canada.
  • Cuixia Liang
    Department of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
  • Xiaoyun Liang
    The School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China.
  • Rongchang Chen
    Key Laboratory of Respiratory Disease of Shenzhen, Shenzhen Institute of Respiratory Disease, Shenzhen People's Hospital (Second Affiliated Hospital of Jinan University, First Affiliated Hospital of South University of Science and Technology of China), Shenzhen, China. Electronic address: chenrc@vip.163.com.
  • Zhenyu Liang
    State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China. Electronic address: 490458234@qq.com.