Advances of surgical robotics: image-guided classification and application

Area of Science:

• Robotics
• Medical Imaging
• Minimally Invasive Surgery

Background:

• Surgical robotics is rapidly advancing, aiming for less traumatic procedures with increased intelligence and autonomy.
• Environmental sensing is a critical challenge for robotic systems in surgery.
• Robot vision relies heavily on image data for environmental information.

Purpose of the Study:

• To review and analyze the application of image technologies in medical purposes for surgical robotics.
• To provide insights and guidance for developing advanced surgical robotic systems.
• To categorize clinical images based on acquisition and tracking frequency.

Main Methods:

• Categorization of clinical images into direct and indirect based on information acquisition.
• Classification of images into continuous, intermittent continuous, and discontinuous based on target-tracking frequency.
• Review of existing surgical robots' characteristics and applications within these categories.

Main Results:

• Established a framework for classifying clinical images relevant to surgical robotics.
• Detailed the characteristics and applications of surgical robots based on image categorization.
• Summarized current evidence on image technology applications in medical robotics.

Conclusions:

• The developed image categorization provides a foundation for understanding surgical robot vision capabilities.
• Insights gained can guide the design and development of more intelligent and autonomous surgical robotic systems.
• Further research into advanced image technologies is crucial for enhancing surgical robot performance.