Needle-Tissue Interaction Force State Estimation for Robotic Surgical Suturing.

Area of Science:

• Robotics
• Surgical Technology
• Biomedical Engineering

Background:

• Robotically Assisted Minimally Invasive Surgery (RAMIS) presents advantages but faces limitations due to non-intuitive interfaces and cost.
• Automating surgical subtasks, like suturing, can enhance efficiency and allow surgeons to focus on procedural planning.
• Accurate modeling of forces during needle-tissue interaction is essential for successful automation, particularly for minimizing tissue deformation.

Purpose of the Study:

• To develop a method for segmenting internal tissue deformation forces from other needle-tissue interaction forces.
• To propose an Unscented Kalman Filter (UKF) capable of modeling these force components during robotic surgery.
• To reduce limitations in RAMIS by improving the automation of surgical subtasks like suturing.

Main Methods:

• Implementation of an Unscented Kalman Filter (UKF) to analyze needle-tissue interactions.
• Experimental setup to drive a needle through tissue samples and measure interaction forces.
• Signal processing techniques to differentiate between tissue deformation forces and other frictional forces.

Main Results:

• The proposed Unscented Kalman Filter successfully modeled the force components during needle insertion.
• The filter effectively segmented the internal tissue deformation force from other interaction forces.
• Demonstrated the potential for minimizing tissue deformation forces in automated suturing.

Conclusions:

• The UKF provides a viable method for accurately modeling needle-tissue interaction forces.
• This approach is a critical step towards enabling automated suturing in RAMIS.
• Reducing tissue deformation through force modeling can improve patient outcomes and surgical precision.