AI Robotic Surgical Arm Control for Anatomical Structure Avoidance
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Solution Overview
Problem
Existing robotic surgical systems lack enhanced imaging, improved treatment planning, risk assessment, robot-assisted navigation, autonomous robotics, intraoperative decision support, and continuous learning capabilities, which hinder precision and safety during surgical procedures.
Innovation Solution
A robotic surgery system equipped with an AI system that utilizes machine learning algorithms to analyze intraoperative data, identify anatomical structures, and adjust robotic arm movements in real-time to minimize tissue damage, while providing improved dexterity and navigation, and enabling continuous learning and improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If machine learning algorithms analyze intraoperative data in real-time to identify anatomical structures and adjust robotic arm movements, then surgical precision and tissue protection are improved, but device complexity increases
Solution Approach 1:
The patent replaces manual mechanical control with AI-based automated control systems. Machine learning algorithms process intraoperative data and autonomously adjust robotic arm movements, substituting the surgeon's manual mechanical control with intelligent automated decision-making to achieve higher precision while managing complexity through software intelligence
Solution Approach 2:
The robotic surgical system incorporates self-service capabilities where the AI system autonomously analyzes real-time surgical data, identifies anatomical structures, and adjusts robotic arm movements without continuous manual intervention. The system monitors and adapts to surgical conditions automatically, reducing the need for constant human input while maintaining high precision
2Adaptability or versatility
If multiple machine learning algorithms process and analyze surgical data continuously, then adaptive manipulation and decision support are improved, but computational resource requirements and system complexity increase
Solution Approach 1:
The patent segments the AI processing system into specialized modules, each handling specific tasks such as anatomical structure identification, surgical data analysis, and robotic control optimization. This modular approach allows continuous adaptive manipulation while managing computational complexity by dividing the processing workload into distinct functional units
Solution Approach 2:
The system performs preliminary actions by pre-processing surgical data and training machine learning models before actual surgical procedures. Historical surgical data is analyzed in advance to create predictive models that can rapidly adapt during real-time operations, reducing the computational burden during critical surgical moments while maintaining high adaptability
3Reliability
If robotic arms are controlled partially autonomously based on real-time AI analysis, then surgical outcomes are improved, but loss of surgeon control and increased automation complexity occur
Solution Approach 1:
The patent implements continuous feedback loops where the AI system monitors surgical outcomes in real-time and adjusts robotic arm movements accordingly. The system receives feedback from surgical data, compares it against predefined success criteria, and autonomously makes minor adjustments to improve outcomes while maintaining the surgeon's overall control direction
Solution Approach 2:
The system applies partial automation by implementing AI control only for specific high-precision tasks such as tissue manipulation and instrument positioning, while leaving broader surgical decisions to the surgeon. This selective automation approach improves reliability in critical areas without completely transferring control to autonomous systems
4Measurement precision
If real-time image recognition and anatomical structure identification are performed, then navigation accuracy is improved, but processing time and computational load increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing and segmenting anatomical structures from surgical images before real-time operations. Machine learning models are trained in advance on extensive anatomical datasets, enabling rapid recognition during actual surgery. This pre-computation approach maintains high identification accuracy while minimizing real-time processing delays
Data Source
AI summary
A surgical robot is coupled to the surgeon console. The surgical robot performs a robotic surgical procedure. The surgical robot includes one or more robotic surgical arms. A control system is coupled to the one or more robotic surgical arms. An artificial intelligence (“AI”) system includes a plurality of machine learning algorithms. The robotic surgical arms are at least partially controlled by the AI system and the control device to process intraoperative data including images captured by cameras and sensor inputs. The machine learning algorithms analyze the intraoperative data in real time, comparing it with stored images and procedural information in image recognition and procedure databases. The one or more machine algorithms enable at least partial identification of anatomical structures. In response to detection of the anatomical structures the AI system at least partially adjusts movement of the robotic surgical arms to avoid critical anatomical structures while performing the robotic surgery procedure to ensure precise targeting at the surgical site while minimizing damage to surrounding tissue at a surgical site. The AI system provides a surgeon with improved dexterity when the surgeon uses the robotic surgical arms at the surgical site, the improved dexterity resulting from at least partially analyzing the intraoperative data in real time by the one or more machine learning algorithms, enabling precise and adaptive manipulation of the robotic surgical arms at the surgical site.