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Vision-Based Suture Tensile Force Estimation in Robotic Surgery.

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  • 1Department of Mechanical, Robotics and Energy Engineering, Dongguk University, 30, Pildong-ro 1gil, Jung-gu, Seoul 04620, Korea.

Sensors (Basel, Switzerland)
|December 30, 2020
PubMed
Summary
This summary is machine-generated.

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Robotics-assisted surgery lacks force feedback. This study proposes a deep learning method using images and robot position to estimate suture tension, enhancing surgical safety.

Area of Science:

  • Robotics
  • Surgical Technology
  • Artificial Intelligence

Background:

  • Robotics-assisted minimally invasive surgery (RMIS) lacks crucial haptic force feedback, increasing the risk of suture breakage.
  • Surgeons currently rely on proprioception and visual cues, informed by training, to estimate suture tension.
  • Absence of direct force sensing is a significant limitation in current RMIS systems.

Purpose of the Study:

  • To develop and validate a deep learning-based method for estimating suture tensile force in RMIS without using physical force sensors.
  • To overcome the lack of force feedback in robotic surgery by leveraging visual and positional data.
  • To enhance surgical safety and precision by providing an indirect measure of suture tension.

Main Methods:

  • A novel deep learning model combining a modified Inception Resnet-V2 and Long Short-Term Memory (LSTM) networks was designed.
Keywords:
force estimationinteraction forcemachine learningminimally invasive surgeryneural networkssuture tensile force

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  • The model was trained and validated using a custom dataset capturing interactions under varying conditions (artificial skins, in vivo/in vitro settings) and multiple viewing angles.
  • Suture tensile force was estimated using only single 2D images and robot end-effector position data.

    • Main Results:

    • The proposed deep learning model successfully estimated suture tensile force with high accuracy.
    • Feasibility was demonstrated across different viewing angles, including angles not encountered during training (10 unseen angles).
    • The system proved effective under diverse conditions, validating its robustness for real-world surgical applications.

    Conclusions:

    • The developed deep learning approach effectively estimates suture tensile force in robotics-assisted surgery, compensating for the lack of haptic feedback.
    • This method offers a promising solution for improving safety and control in minimally invasive robotic procedures.
    • Further integration of this technology could significantly advance the capabilities of robotic surgical systems.