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Vision-Based Suture Tensile Force Estimation in Robotic Surgery.

Won-Jo Jung¹, Kyung-Soo Kwak¹, Soo-Chul Lim¹

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

Sensors (Basel, Switzerland)

|December 30, 2020

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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 estimation interaction force machine learning minimally invasive surgery neural networks suture 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.