Design of intelligent human-machine collaborative robot-assisted craniotomy system

Area of Science:

• Robotics in Medicine
• Neurosurgical Technology
• Human-Machine Interaction

Background:

• Craniotomy procedures require high precision and safety.
• Existing methods may have limitations in accuracy and efficiency.
• Advancements in robotics offer potential solutions for surgical enhancement.

Purpose of the Study:

• To develop an intelligent human-machine collaborative robot-assisted craniotomy system.
• To experimentally evaluate the system's accuracy, efficiency, and safety.

Main Methods:

• Integrated a UR5 robotic arm, force sensor, medical drill, and optical navigation system.
• Developed a custom navigation procedure and graphical user interface (GUI).
• Tested the system on 3D-printed skull models and Bama mini pigs.

Main Results:

• The robot-assisted system showed significantly reduced average positioning error (1.87 mm vs 3.14 mm on models; 3.26 mm vs 4.39 mm on pigs).
• Shorter procedure times were observed with the robot system (6.64 min vs 8.06 min on models; 11.83 min vs 26.10 min on pigs).
• The system provided sensitive force feedback and demonstrated a trend towards reduced tissue injury.

Conclusions:

• The developed human-machine collaborative robot-assisted craniotomy system performs fluently with sensitive force feedback.
• The system outperformed manual surgery in accuracy, efficiency, and safety during experimental tests.
• Further research is needed to confirm its applicability in clinical neurosurgery.