Surgical Robot Supervised Automation Multi-Modal Imaging
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Solution Overview
Problem
Current robotic surgery systems are limited by the master-slave paradigm, constraining the robot's speed and dexterity to that of the surgeon, and lack true automation, especially in tasks requiring high precision and repetition, and do not integrate visible light images with other modalities for control.
Innovation Solution
The system employs supervised automation with an evidence-based decision support algorithm that analyzes clinical data from both manual and robot-assisted surgeries to identify critical information for automation, using visual servoing with real-time feedback from cameras and other sensors to adapt and improve surgical tasks, allowing the robot to operate in master-slave, semi-autonomous, and supervised autonomous modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If master-slave control is used, then the surgeon can directly control the robot, but the robot's speed and dexterity are constrained to that of the surgeon
Solution Approach 1:
The system enables the robot to autonomously perform surgical tasks by integrating multi-modal imaging data and automated control algorithms. The robot independently identifies anatomical structures, plans surgical paths, and executes procedures without continuous surgeon intervention, thereby achieving self-service operation that overcomes the speed and dexterity constraints of master-slave control.
Solution Approach 2:
The patent replaces the mechanical master-slave control linkage with an automated decision-support system that integrates multi-modal imaging (visible light, infrared, ultrasound) and computational algorithms. This substitution eliminates the direct mechanical coupling between surgeon and robot, allowing the robot to operate at its full computational and mechanical capabilities rather than being limited by surgeon reaction time and precision.
2Reliability
If master-slave control is used, then the surgeon can control the robot, but the dexterity is limited by the surgeon's skill
Solution Approach 1:
The robot autonomously performs high-dexterity tasks such as suture placement, tissue manipulation, and precise cutting by integrating multi-modal imaging data with automated control algorithms. The system independently identifies anatomical structures, calculates optimal surgical paths, and executes procedures with superhuman precision, eliminating the dexterity limitations imposed by surgeon skill levels.
Solution Approach 2:
The patent replaces the surgeon's manual control with an automated decision-support system that integrates visible light, infrared, and ultrasound imaging with computational algorithms. This substitution enables the robot to perform complex dexterous maneuvers with precision beyond human capability, while the surgeon retains supervisory control for decision-making.
3Manufacturing precision
If automation is implemented, then precision and consistency are improved, but the system complexity increases
Solution Approach 1:
The system merges multiple imaging modalities (visible light cameras, infrared cameras, ultrasound sensors) and computational functions (image processing, anatomical structure identification, surgical path planning, robot control) into an integrated automated decision-support platform. This consolidation achieves high surgical precision through multi-modal data fusion while managing system complexity through unified architecture and centralized control.
Solution Approach 2:
The automated system performs multiple functions including image acquisition from various modalities, real-time image processing, anatomical structure identification, surgical path planning, and robot control within a single integrated platform. This multi-functionality achieves comprehensive precision across all surgical tasks while reducing overall system complexity compared to separate specialized systems.
4Measurement precision
If multi-modal imaging is integrated, then tracking accuracy is improved, but the device complexity increases
Solution Approach 1:
The system merges visible light cameras, infrared cameras, and ultrasound sensors into a unified imaging platform that simultaneously captures multiple modalities. The integrated system processes and fuses data from all sensors in real-time to achieve superior tracking accuracy for anatomical structures and surgical tools, while managing sensor integration complexity through centralized control and coordinated operation.
Solution Approach 2:
The imaging system performs multiple functions using a single integrated platform: visible light imaging for anatomical structure visualization, infrared imaging for thermal and sub-surface tissue information, and ultrasound for deep tissue penetration and real-time imaging. This multi-functionality achieves comprehensive tracking accuracy across different tissue types and depths while avoiding the complexity of separate specialized systems.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the efficiency and effectiveness of surgical tasks by automating high-dexterity, precision, and repetitive procedures, such as anastomosis, and improves upon the limitations of individual surgeon experience, enabling the robot to perform tasks with greater precision and consistency than human surgeons.
Implementation Method 1
An example of non-visible spectrum image is the near-infrared fluorescent (NIR) image
Implementation Method 2
3D depth information could be extracted from stereo triangulation algorithm
Implementation Method 3
3D depth information could also be obtained through structured-light 3D scanners
Data Source
AI summary
Described herein are an apparatus and methods for automating subtasks in surgery and interventional medical procedures. The apparatus consists of a robotic positioning platform, an operating system with automation programs, and end-effector tools to carry out a task under supervised autonomy. The operating system executes an automation program, based on one or a fusion of two or more imaging modalities, guides real-time tracking of mobile and deformable targets in unstructured environment while the end-effector tools execute surgical interventional subtasks that require precision, accuracy, maneuverability and repetition. The apparatus and methods make these medical procedures more efficient and effective allowing a wider access and more standardized outcomes and improved safety.