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Supervised Autonomous Electrosurgery via Biocompatible Near-Infrared Tissue Tracking Techniques.

H Saeidi1, J Ge1, M Kam1

  • 1Mechanical Engineering Department, University of Maryland, College Park, MD 20742, USA., Fischell Institute for Biomedical Devices and the Marlene and Stewart Greenebaum Cancer Center.

IEEE Transactions on Medical Robotics and Bionics
|January 18, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel autonomous robotic surgery strategy using Near-Infrared markers for precise soft tissue control. The Smart Tissue Autonomous Robot (STAR) system significantly reduces incision errors compared to existing methods.

Keywords:
Medical Robots and SystemsSupervised Autonomous ControlSurgical Robotics Planning

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Area of Science:

  • Robotics
  • Surgical Technology
  • Biomedical Engineering

Background:

  • Autonomous robotic surgery aims to enhance patient outcomes through automation.
  • Controlling soft tissues during surgery is challenging due to their deformability and lack of structure.
  • Existing methods struggle with precise intraoperative soft tissue tracking and robot control.

Purpose of the Study:

  • To develop a supervised autonomous 3D path planning, filtering, and control strategy for robotic surgery.
  • To enable precise and consistent incisions on complex 3D soft tissues using Near-Infrared (NIR) marking.
  • To improve the accuracy of autonomous robotic surgery systems.

Main Methods:

  • Utilized biocompatible Near-Infrared (NIR) marking methods for soft tissue tracking.
  • Developed a supervised autonomous 3D path planning, filtering, and control strategy.
  • Implemented the strategy on the Smart Tissue Autonomous Robot (STAR) system.
  • Tested the system on cadaver porcine tongue samples.

Main Results:

  • The STAR system reduced surface incision error by 40.03% compared to da Vinci teleoperation.
  • Depth incision error was reduced by 51.5% compared to da Vinci teleoperation.
  • The proposed strategy reduced incision depth error by 48.58% compared to linear interpolation between NIR markers by using 3D tissue surface information.

Conclusions:

  • The developed autonomous strategy significantly improves incision accuracy in soft tissues.
  • NIR marking combined with 3D path planning offers a promising approach for autonomous robotic surgery.
  • This technology has the potential to reduce human-induced errors and enhance surgical precision.