Detection of peripherally inserted central catheter (PICC) in chest X-ray images: A multi-task deep learning model.

Journal: Computer methods and programs in biomedicine
PMID: 32738678

Abstract

BACKGROUND AND OBJECTIVE: Peripherally inserted central catheter (PICC) is a novel drug delivery mode which has been widely used in clinical practice. However, long-term retention and some improper actions of patients may cause some severe complications of PICC, such as the drift and prolapse of its catheter. Clinically, the postoperative care of PICC is mainly completed by nurses. However, they cannot recognize the correct position of PICC from X-ray chest images as soon as the complications happen, which may lead to improper treatment. Therefore, it is necessary to identify the position of the PICC catheter as soon as these complications occur. Here we proposed a novel multi-task deep learning framework to detect PICC automatically through X-ray images, which could help nurses to solve this problem.

Authors

  • Dingding Yu
    School of Mathematical Sciences, Zhejiang University. Hangzhou, Zhejiang Province, China, 310027.
  • Kaijie Zhang
    Graduate School, Hebei North University, 075000 Zhangjiakou, Hebei, China.
  • Lingyan Huang
    Department of Radiation Oncology, Zhejiang Quhua Hospital, Quzhou, Zhejiang Province, China, 324000.
  • Bonan Zhao
    School of Mathematical Sciences, Zhejiang University. Hangzhou, Zhejiang Province, China, 310027.
  • Xiaoshan Zhang
    School of Mathematical Sciences, Zhejiang University. Hangzhou, Zhejiang Province, China, 310027.
  • Xin Guo
    Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong.
  • Miaomiao Li
    School of Computer Science, National University of Defense Technology, Changsha 410073, China. Electronic address: miaomiaolinudt@gmail.com.
  • Zheng Gu
    Department of Vascular Surgery, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China, 310009.
  • Guosheng Fu
    Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province. Sir Run Shaw Hospital, School of Medicine, Zhejiang University. Hangzhou, Zhejiang Province, China, 310016.
  • Minchun Hu
    Department of Radiation Oncology, Zhejiang Quhua Hospital, Quzhou, Zhejiang Province, China, 324000.
  • Yan Ping
    Department of Radiation Oncology, Zhejiang Quhua Hospital, Quzhou, Zhejiang Province, China, 324000.
  • Ye Sheng
    Department of Nursing, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China, 310009.
  • Zhenjie Liu
    Department of Vascular Surgery, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China, 310009. Electronic address: lawson4001@zju.edu.cn.
  • Xianliang Hu
    School of Mathematical Sciences, Zhejiang University. Hangzhou, Zhejiang Province, China, 310027. Electronic address: xlhu@zju.edu.cn.
  • Ruiyi Zhao
    Department of Nursing, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China, 310009. Electronic address: 2192028@zju.edu.cn.

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