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A Confidence-Based Supervised-Autonomous Control Strategy for Robotic Vaginal Cuff Closure.

Michael Kam1, Hamed Saeidi1, Michael H Hsieh2

  • 1Dep. of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21211, USA.

IEEE International Conference on Robotics and Automation : ICRA : [Proceedings]. IEEE International Conference on Robotics and Automation
|November 29, 2021
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Summary
This summary is machine-generated.

This study introduces a confidence-based supervised autonomous suturing method for robotic surgery. The Smart Tissue Autonomous Robot (STAR) achieves high accuracy and consistency, improving surgical outcomes with human-robot collaboration.

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Area of Science:

  • Robotics
  • Surgical Technology
  • Artificial Intelligence in Medicine

Background:

  • Autonomous robotic suturing offers potential improvements in surgical accuracy, repeatability, and consistency over manual methods.
  • Full autonomy in complex surgical environments remains challenging, necessitating human supervision for safety.
  • Existing robotic systems require further development to enhance collaborative capabilities and ensure reliable performance.

Purpose of the Study:

  • To develop and evaluate a confidence-based supervised autonomous suturing method for robotic surgery.
  • To enable collaborative operation between the Smart Tissue Autonomous Robot (STAR) and human surgeons for enhanced autonomy.
  • To improve the safety and effectiveness of robotic suturing in complex surgical tasks.

Main Methods:

  • Development of a confidence-based control algorithm for autonomous robotic suturing.
  • Implementation of a system where STAR operates autonomously when confident and seeks human assistance otherwise.
  • Evaluation of the method using robotic suturing on synthetic vaginal cuff tissues, comparing results with surgeon-performed closures.

Main Results:

  • The confidence-based method achieved 94.74% accuracy in predicting successful autonomous suture placement.
  • With 25% human intervention, STAR reached 98.1% suture placement accuracy, significantly outperforming fully autonomous suturing (85.4%).
  • STAR demonstrated 1.6x better consistency in suture spacing and 1.8x better consistency in suture bite sizes compared to manual suturing.

Conclusions:

  • The proposed confidence-based supervised autonomous suturing method enhances the accuracy and consistency of robotic surgery.
  • Human-robot collaboration significantly improves suture placement accuracy, balancing autonomy with safety.
  • This approach represents a significant advancement in achieving higher degrees of autonomy in robotic surgical procedures.