AI-Guided Robotic Surgical Arms for Anatomical Structure Avoidance
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Solution Overview
Problem
Existing robotic surgery 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 a surgeon console, image recognition database, and machine learning algorithms that analyze intraoperative data to identify anatomical structures, adjust robotic arm movements, and provide real-time decision support, enabling precise targeting and minimizing tissue damage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If machine learning algorithms are integrated into the robotic surgery system to analyze intraoperative data in real time, then surgical precision and safety are improved, but device complexity increases
Solution Approach 1:
The patent introduces an AI system as an intermediary component that processes intraoperative data from multiple sensors and cameras. This intermediary layer analyzes data in real-time and provides guidance to the robotic arms, enabling enhanced precision without requiring direct complex control mechanisms in the robotic arms themselves.
Solution Approach 2:
The patent replaces traditional mechanical control systems with an AI-based control approach. Instead of relying solely on mechanical precision and physical constraints, the system uses machine learning algorithms to interpret surgical context, identify anatomical structures, and autonomously adjust robotic arm movements, thereby achieving higher precision through intelligent control rather than mechanical complexity.
2Reliability
If real-time image recognition and machine learning analysis are implemented, then treatment planning and decision support are improved, but processing time and computational requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing and storing intraoperative data from multiple sources (images, sensor inputs, procedural information) in databases before the actual surgical decision-making moment. The machine learning models are pre-trained on extensive datasets, enabling them to quickly analyze new data during surgery without requiring time-consuming computations in real-time.
Solution Approach 2:
The system implements continuous feedback loops where the AI analyzes intraoperative data and provides real-time guidance to the robotic arms. This feedback mechanism enables the system to adapt to changing surgical conditions dynamically, improving decision support accuracy while maintaining efficient processing speeds through optimized data flow and real-time computation.
3Manufacturing precision
If autonomous control of robotic arms is increased based on AI analysis, then surgical precision is improved, but loss of surgeon control and adaptability worsens
Solution Approach 1:
The patent implements partial autonomy where the AI system handles specific precision-critical tasks such as anatomical structure identification, movement adjustment to avoid critical structures, and precise positioning. The surgeon retains overall control and can override AI recommendations when needed, maintaining adaptability while benefiting from enhanced precision in targeted areas.
Solution Approach 2:
The robotic arms are designed with multi-functionality, capable of performing various surgical tasks while being guided by the AI system. The system can adapt to different surgical scenarios and anatomical variations through its machine learning capabilities, providing universal applicability across different procedures while maintaining the surgeon's ability to customize the approach based on real-time needs.
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.