Vascular Environment Identification Based on Multi-dimensional Data Fusion for Interventional Surgical Robots

Dong Yang, Wei Wei, Jiaqian Li, Nan Xiao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Vascular interventional surgery is the most commonly used method for the treatment of cardio-vascular and cerebrovascular diseases. Master-slave interventional surgical robot is a promising technology, which can further improve the accuracy and safety of surgery. However, imperfect measurement of catheter force remains a surgical risk. Inspired by the function of insect antennae, a thin-film force sensing device was installed in the catheter head. Combined with the pressure sensor in the catheter clamping device, the LSTM network was used to predict and classify the curvature of the current passing vessel, and the recognition accuracy was 97%. In the process of robotic surgery, real-time feedback of current pressure information and vascular curvature information can enhance the doctor's judgment of the operation state and improve the safety of surgery.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1738-1744
Number of pages7
ISBN (Electronic)9781665481090
DOIs
Publication statusPublished - 2022
Event2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022 - Jinghong, China
Duration: 5 Dec 20229 Dec 2022

Publication series

Name2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

Conference

Conference2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022
Country/TerritoryChina
CityJinghong
Period5/12/229/12/22

Keywords

  • LSTM classification
  • force detection
  • robot-assisted surgery
  • vascular interventional surgical robot

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