Early severity prediction of BPD for premature infants from chest X-ray images using deep learning: A study at the 28th day of oxygen inhalation.

Journal: Computer methods and programs in biomedicine
PMID: 35576685

Abstract

BACKGROUND AND OBJECTIVE: Bronchopulmonary dysplasia is a common respiratory disease in premature infants. The severity is diagnosed at the 56th day after birth or discharge by analyzing the clinical indicators, which may cause the delay of the best treatment opportunity. Thus, we proposed a deep learning-based method using chest X-ray images of the 28th day of oxygen inhalation for the early severity prediction of bronchopulmonary dysplasia in clinic.

Authors

  • Wenyu Xing
    Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai 200438, China; Human Phenome Institute, Fudan University, Shanghai 200438, China.
  • Wen He
    Department of Ultrasound, Beijing Tian Tan Hospital, Capital Medical University, Beijing 100070, China.
  • Xiaoling Li
    Department of Infections，Beijing Hospital of Traditional Chinese Medicine, Affiliated to the Capital Medical University, No. 23, Back Road of the Art Gallery, Dongcheng District, Beijing 100010, China.
  • Jiangang Chen
  • Yun Cao
    Department of Neonatology, Children's Hospital of Fudan University, Shanghai 201102, China.
  • Wenhao Zhou
    The Molecular Genetic Diagnosis Center, Shanghai Key Lab of Birth Defect, Translational Medicine Research Center of Children Development and Diseases, Pediatrics Research Institute, Shanghai, China.
  • Quanli Shen
    Department of Radiology, Children's Hospital of Fudan University, Shanghai 201102, China.
  • Xiaobo Zhang
    School of Chemistry and Chemical Engineering, Shandong University of Technology, Zibo 255049, P. R. China. liyueyun@sdut.edu.cn.
  • Dean Ta

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