A Respiratory Signal Monitoring Method Based on Dual-Pathway Deep Learning Networks in Image-Guided Robotic-Assisted Intervention System.

Journal: The international journal of medical robotics + computer assisted surgery : MRCAS
PMID: 39718347

Abstract

BACKGROUND: Percutaneous puncture procedures, guided by image-guided robotic-assisted intervention (IGRI) systems, are susceptible to disruptions in patients' respiratory rhythm due to factors such as pain and psychological distress.

Authors

  • Xiaodong Wang
    Cardiovascular Department, TEDA International Cardiovascular Hospital, Tianjin, China.
  • Jianjun Zhu
    Department of Radiology, Zhongda Hospital Southeast University, Nanjing, China.
  • Yong Wang
    State Key Laboratory of Chemical Biology and Drug Discovery, Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University Hunghom Kowloon Hong Kong P. R. China kwok-yin.wong@polyu.edu.hk.
  • Cheng Wang
    Department of Pathology, Dalhousie University, Halifax, NS, Canada.
  • Peng Chen
  • Pengju Lyu
    R&D Department, Hanglok-Tech Co., Ltd., Hengqin, China.
  • Jun Xu
    Department of Nephrology, The Affiliated Baiyun Hospital of Guizhou Medical University, Guizhou, China.
  • Gao-Jun Teng
    Center of Interventional Radiology & Vascular Surgery, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, 87 Dingjiaqiao Road, Nanjing 210009, China. Electronic address: gjteng@vip.sina.com.

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