Robotic Surgery Trajectory Planning for Tissue Deformation Tracking
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Solution Overview
Problem
Current robotic surgery systems lack the ability to perform fully or partially autonomous surgical procedures due to limitations in designing and executing safe and effective movements, both pre-operatively and in real time, primarily because they rely on human control and lack advanced decision-making capabilities.
Innovation Solution
A robotic surgery system that incorporates a machine learning system to analyze data from surgical procedures, providing feedback and control signals to improve surgical planning and execution, including the use of imaging systems to evaluate treatment trajectories and predict post-operative organ function, and a flexible panel with spatial tracking sensors to track tissue deformation in real time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If robotic surgery systems rely on human control for full autonomy, then human decision-making capability is limited, but system complexity and cost increase
Solution Approach 1:
The robotic surgery system performs self-evaluation by automatically assessing treatment trajectories and predicting post-operative organ function using machine learning models. The system autonomously generates scores for different surgical approaches and selects optimal trajectories without requiring constant human intervention, enabling the robot to serve itself in decision-making processes.
Solution Approach 2:
The system implements closed-loop feedback by continuously monitoring surgical progress, comparing actual outcomes with predicted outcomes, and automatically adjusting treatment trajectories in real-time. The machine learning models learn from historical surgical data and intraoperative feedback to improve autonomous decision-making, reducing the need for complex human-in-the-loop control.
2Manufacturing precision
If robotic surgery systems use human teleoperation, then human imperfection limits surgical precision, but real-time human decision-making is slower than automated systems
Solution Approach 1:
The system performs pre-operative planning and treatment trajectory optimization before the surgical procedure begins. Machine learning models predict the best surgical approaches based on patient-specific anatomy and historical data, allowing the system to prepare optimal trajectories in advance rather than making decisions during the time-critical surgical procedure.
Solution Approach 2:
The system replaces human cognitive decision-making with automated machine learning algorithms that evaluate treatment trajectories and predict outcomes. The mechanical substitution of human judgment with computational models enables faster, more consistent, and equally precise surgical planning without being subject to human fatigue or imperfection.
3Measurement precision
If the system evaluates multiple treatment trajectories, then surgical planning accuracy improves, but computational time and processing power increase
Solution Approach 1:
The system evaluates multiple treatment trajectories but uses machine learning models to rank and prioritize them, focusing computational resources on the top candidate trajectories rather than exhaustively analyzing all possible options. This partial evaluation approach maintains high accuracy by considering the most promising trajectories while reducing unnecessary computational overhead.
Solution Approach 2:
The machine learning models use parameter optimization techniques to efficiently evaluate treatment trajectories by changing key parameters such as cutting depth, trajectory angle, and tissue deformation thresholds. This allows the system to assess multiple trajectories rapidly by varying critical parameters rather than performing full simulations for each option.
Data Source
AI summary
Teleoperative, partially automated, and fully automated robotic surgery systems and methods are described herein. These systems and methods relate to at least improvement of robotic movements, three dimensional tracking and pose correction for robots interacting with deformable objections, controlling and optimizing the redundant axis of a seven degree of freedom robotic arm, virtual robotic surgery and simulation, and task coordination and optimization for multi-robot surgery.